@inproceedings{115, abstract = {{Whenever customers have to decide between different instances of the same product, they are interested in buying the best product. In contrast, companies are interested in reducing the construction effort (and usually as a consequence thereof, the quality) to gain profit. The described setting is widely known as opposed preferences in quality of the product and also applies to the context of service-oriented computing. In general, service-oriented computing emphasizes the construction of large software systems out of existing services, where services are small and self-contained pieces of software that adhere to a specified interface. Several implementations of the same interface are considered as several instances of the same service. Thereby, customers are interested in buying the best service implementation for their service composition wrt. to metrics, such as costs, energy, memory consumption, or execution time. One way to ensure the service quality is to employ certificates, which can come in different kinds: Technical certificates proving correctness can be automatically constructed by the service provider and again be automatically checked by the user. Digital certificates allow proof of the integrity of a product. Other certificates might be rolled out if service providers follow a good software construction principle, which is checked in annual audits. Whereas all of these certificates are handled differently in service markets, what they have in common is that they influence the buying decisions of customers. In this paper, we review state-of-the-art developments in certification with respect to service-oriented computing. We not only discuss how certificates are constructed and handled in service-oriented computing but also review the effects of certificates on the market from an economic perspective.}}, author = {{Jakobs, Marie-Christine and Krämer, Julia and van Straaten, Dirk and Lettmann, Theodor}}, booktitle = {{The Ninth International Conferences on Advanced Service Computing (SERVICE COMPUTATION)}}, editor = {{Marcelo De Barros, Janusz Klink,Tadeus Uhl, Thomas Prinz}}, pages = {{7--12}}, title = {{{Certification Matters for Service Markets}}}, year = {{2017}}, } @inproceedings{1158, abstract = {{In this paper, we present the annotation challenges we have encountered when working on a historical language that was undergoing elaboration processes. We especially focus on syntactic ambiguity and gradience in Middle Low German, which causes uncertainty to some extent. Since current annotation tools consider construction contexts and the dynamics of the grammaticalization only partially, we plan to extend CorA – a web-based annotation tool for historical and other non-standard language data – to capture elaboration phenomena and annotator unsureness. Moreover, we seek to interactively learn morphological as well as syntactic annotations.}}, author = {{Seemann, Nina and Merten, Marie-Luis and Geierhos, Michaela and Tophinke, Doris and Hüllermeier, Eyke}}, booktitle = {{Proceedings of the Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature}}, location = {{Vancouver, BC, Canada}}, pages = {{40--45}}, publisher = {{Association for Computational Linguistics (ACL)}}, title = {{{Annotation Challenges for Reconstructing the Structural Elaboration of Middle Low German}}}, doi = {{10.18653/v1/W17-2206}}, year = {{2017}}, } @misc{5694, author = {{Schnitker, Nino Noel}}, publisher = {{Universität Paderborn}}, title = {{{Genetischer Algorithmus zur Erstellung von Ensembles von Nested Dichotomies}}}, year = {{2017}}, } @inproceedings{5722, author = {{Gupta, Pritha and Hetzer, Alexander and Tornede, Tanja and Gottschalk, Sebastian and Kornelsen, Andreas and Osterbrink, Sebastian and Pfannschmidt, Karlson and Hüllermeier, Eyke}}, location = {{Rostock}}, title = {{{jPL: A Java-based Software Framework for Preference Learning}}}, year = {{2017}}, } @misc{5724, author = {{Hetzer, Alexander and Tornede, Tanja}}, publisher = {{Universität Paderborn}}, title = {{{Solving the Container Pre-Marshalling Problem using Reinforcement Learning and Structured Output Prediction}}}, year = {{2017}}, } @inproceedings{71, abstract = {{Today, software verification tools have reached the maturity to be used for large scale programs. Different tools perform differently well on varying code. A software developer is hence faced with the problem of choosing a tool appropriate for her program at hand. A ranking of tools on programs could facilitate the choice. Such rankings can, however, so far only be obtained by running all considered tools on the program.In this paper, we present a machine learning approach to predicting rankings of tools on programs. The method builds upon so-called label ranking algorithms, which we complement with appropriate kernels providing a similarity measure for programs. Our kernels employ a graph representation for software source code that mixes elements of control flow and program dependence graphs with abstract syntax trees. Using data sets from the software verification competition SV-COMP, we demonstrate our rank prediction technique to generalize well and achieve a rather high predictive accuracy (rank correlation > 0.6).}}, author = {{Czech, Mike and Hüllermeier, Eyke and Jakobs, Marie-Christine and Wehrheim, Heike}}, booktitle = {{Proceedings of the 3rd International Workshop on Software Analytics}}, pages = {{23--26}}, title = {{{Predicting Rankings of Software Verification Tools}}}, doi = {{10.1145/3121257.3121262}}, year = {{2017}}, } @techreport{72, abstract = {{Software verification competitions, such as the annual SV-COMP, evaluate software verification tools with respect to their effectivity and efficiency. Typically, the outcome of a competition is a (possibly category-specific) ranking of the tools. For many applications, such as building portfolio solvers, it would be desirable to have an idea of the (relative) performance of verification tools on a given verification task beforehand, i.e., prior to actually running all tools on the task.In this paper, we present a machine learning approach to predicting rankings of tools on verification tasks. The method builds upon so-called label ranking algorithms, which we complement with appropriate kernels providing a similarity measure for verification tasks. Our kernels employ a graph representation for software source code that mixes elements of control flow and program dependence graphs with abstract syntax trees. Using data sets from SV-COMP, we demonstrate our rank prediction technique to generalize well and achieve a rather high predictive accuracy. In particular, our method outperforms a recently proposed feature-based approach of Demyanova et al. (when applied to rank predictions). }}, author = {{Czech, Mike and Hüllermeier, Eyke and Jakobs, Marie-Christine and Wehrheim, Heike}}, title = {{{Predicting Rankings of Software Verification Competitions}}}, year = {{2017}}, } @misc{10589, author = {{Fürnkranz, J. and Hüllermeier, Eyke}}, booktitle = {{Encyclopedia of Machine Learning and Data Mining}}, pages = {{1000--1005}}, title = {{{Preference Learning}}}, year = {{2017}}, } @inbook{10784, author = {{Fürnkranz, J. and Hüllermeier, Eyke}}, booktitle = {{Encyclopedia of Machine Learning and Data Mining}}, editor = {{Sammut, C. and Webb, G.I.}}, pages = {{1000--1005}}, publisher = {{Springer}}, title = {{{Preference Learning}}}, volume = {{107}}, year = {{2017}}, } @inproceedings{1180, abstract = {{These days, there is a strong rise in the needs for machine learning applications, requiring an automation of machine learning engineering which is referred to as AutoML. In AutoML the selection, composition and parametrization of machine learning algorithms is automated and tailored to a specific problem, resulting in a machine learning pipeline. Current approaches reduce the AutoML problem to optimization of hyperparameters. Based on recursive task networks, in this paper we present one approach from the field of automated planning and one evolutionary optimization approach. Instead of simply parametrizing a given pipeline, this allows for structure optimization of machine learning pipelines, as well. We evaluate the two approaches in an extensive evaluation, finding both approaches to have their strengths in different areas. Moreover, the two approaches outperform the state-of-the-art tool Auto-WEKA in many settings.}}, author = {{Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}}, booktitle = {{27th Workshop Computational Intelligence}}, location = {{Dortmund}}, title = {{{Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization}}}, year = {{2017}}, } @inproceedings{15397, author = {{Melnikov, Vitaly and Hüllermeier, Eyke}}, booktitle = {{in Proceedings 27th Workshop Computational Intelligence, Dortmund Germany}}, editor = {{Hoffmann, F. and Hüllermeier, Eyke and Mikut, R.}}, pages = {{1--12}}, publisher = {{KIT Scientific Publishing}}, title = {{{Optimizing the structure of nested dichotomies. A comparison of two heuristics}}}, year = {{2017}}, } @inproceedings{15399, author = {{Czech, M. and Hüllermeier, Eyke and Jacobs, M.C. and Wehrheim, Heike}}, booktitle = {{in Proceedings ESEC/FSE Workshops 2017 - 3rd ACM SIGSOFT, International Workshop on Software Analytics (SWAN 2017), Paderborn Germany}}, title = {{{Predicting rankings of software verification tools}}}, year = {{2017}}, } @inproceedings{15110, author = {{Couso, Ines and Dubois, D. and Hüllermeier, Eyke}}, booktitle = {{in Proceedings SUM 2017, 11th International Conference on Scalable Uncertainty Management, Granada, Spain}}, pages = {{3--16}}, publisher = {{Springer}}, title = {{{Maximum likelihood estimation and coarse data}}}, year = {{2017}}, } @inproceedings{10204, author = {{Ewerth, Ralph and Springstein, M. and Müller, E. and Balz, A. and Gehlhaar, J. and Naziyok, T. and Dembczynski, K. and Hüllermeier, Eyke}}, booktitle = {{Proc. IEEE Int. Conf. on Multimedia and Expo (ICME 2017)}}, pages = {{919--924}}, title = {{{Estimating relative depth in single images via rankboost}}}, year = {{2017}}, } @inproceedings{10205, author = {{Ahmadi Fahandar, Mohsen and Hüllermeier, Eyke and Couso, Ines}}, booktitle = {{Proc. 34th Int. Conf. on Machine Learning (ICML 2017)}}, pages = {{1078--1087}}, title = {{{Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent Coarsening}}}, year = {{2017}}, } @inproceedings{10206, author = {{Mohr, Felix and Lettmann, Theodor and Hüllermeier, Eyke}}, booktitle = {{Proc. 40th Annual German Conference on Advances in Artificial Intelligence (KI 2017)}}, pages = {{193--206}}, title = {{{Planning with Independent Task Networks}}}, doi = {{10.1007/978-3-319-67190-1_15}}, year = {{2017}}, } @inproceedings{10207, author = {{Czech, M. and Hüllermeier, Eyke and Jakobs, M.-C. and Wehrheim, Heike}}, booktitle = {{Proc. 3rd ACM SIGSOFT Int. I Workshop on Software Analytics (SWAN@ESEC/SIGSOFT FSE 2017}}, pages = {{23--26}}, title = {{{Predicting rankings of software verification tools}}}, year = {{2017}}, } @inproceedings{10208, author = {{Couso, Ines and Dubois, D. and Hüllermeier, Eyke}}, booktitle = {{Proc. 11th Int. Conf. on Scalable Uncertainty Management (SUM 2017)}}, pages = {{3--16}}, title = {{{Maximum Likelihood Estimation and Coarse Data}}}, year = {{2017}}, } @inproceedings{10209, author = {{Ahmadi Fahandar, Mohsen and Hüllermeier, Eyke}}, booktitle = {{Proc. AAAI 2017, 32nd AAAI Conference on Artificial Intelligence}}, title = {{{Learning to Rank based on Analogical Reasoning}}}, year = {{2017}}, } @inproceedings{10212, author = {{Hoffmann, F. and Hüllermeier, Eyke and Mikut, R.}}, title = {{{(Hrsg.) Proceedings 27. Workshop Computational Intelligence, KIT Scientific Publishing, Karlsruhe, Germany 2017}}}, year = {{2017}}, } @inproceedings{10213, author = {{Melnikov, Vitaly and Hüllermeier, Eyke}}, booktitle = {{Proceedings 27. Workshop Computational Intelligence, Dortmund, Germany 2017}}, pages = {{1--12}}, title = {{{Optimizing the Structure of Nested Dichotomies: A Comparison of Two Heuristics}}}, year = {{2017}}, } @inproceedings{10216, author = {{Shaker, Ammar and Heldt, W. and Hüllermeier, Eyke}}, booktitle = {{Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases, Skopje, Macedonia}}, title = {{{Learning TSK Fuzzy Rules from Data Streams}}}, year = {{2017}}, } @article{10267, author = {{Bräuning, M. and Hüllermeier, Eyke and Keller, T. and Glaum, M.}}, journal = {{European Journal of Operational Research}}, number = {{1}}, pages = {{295--306}}, title = {{{Lexicographic preferences for predictive modeling of human decision making. A new machine learning method with an application in accounting}}}, volume = {{258}}, year = {{2017}}, } @article{10268, author = {{Platenius, M.-C. and Shaker, Ammar and Becker, M. and Hüllermeier, Eyke and Schäfer, W.}}, journal = {{IEEE Transactions on Software Engineering}}, number = {{8}}, pages = {{739--759}}, title = {{{Imprecise Matching of Requirements Specifications for Software Services Using Fuzzy Logic}}}, volume = {{43}}, year = {{2017}}, } @article{10269, author = {{Hüllermeier, Eyke}}, journal = {{The Computing Research Repository (CoRR)}}, title = {{{From Knowledge-based to Data-driven Modeling of Fuzzy Rule-based Systems: A Critical Reflection}}}, year = {{2017}}, } @article{24154, author = {{Ramaswamy, Arunselvan and Bhatnagar, Shalabh}}, journal = {{Stochastics}}, number = {{8}}, pages = {{1173--1187}}, publisher = {{Taylor \& Francis}}, title = {{{Stochastic recursive inclusion in two timescales with an application to the lagrangian dual problem}}}, volume = {{88}}, year = {{2016}}, } @article{3318, author = {{Melnikov, Vitalik and Hüllermeier, Eyke and Kaimann, Daniel and Frick, Bernd and Gupta, Pritha }}, issn = {{2083-8476}}, journal = {{Schedae Informaticae}}, publisher = {{Uniwersytet Jagiellonski - Wydawnictwo Uniwersytetu Jagiellonskiego}}, title = {{{Pairwise versus Pointwise Ranking: A Case Study}}}, doi = {{10.4467/20838476si.16.006.6187}}, volume = {{25}}, year = {{2016}}, } @article{190, abstract = {{Today, software components are provided by global markets in the form of services. In order to optimally satisfy service requesters and service providers, adequate techniques for automatic service matching are needed. However, a requester’s requirements may be vague and the information available about a provided service may be incomplete. As a consequence, fuzziness is induced into the matching procedure. The contribution of this paper is the development of a systematic matching procedure that leverages concepts and techniques from fuzzy logic and possibility theory based on our formal distinction between different sources and types of fuzziness in the context of service matching. In contrast to existing methods, our approach is able to deal with imprecision and incompleteness in service specifications and to inform users about the extent of induced fuzziness in order to improve the user’s decision-making. We demonstrate our approach on the example of specifications for service reputation based on ratings given by previous users. Our evaluation based on real service ratings shows the utility and applicability of our approach.}}, author = {{Platenius, Marie Christin and Shaker, Ammar and Becker, Matthias and Hüllermeier, Eyke and Schäfer, Wilhelm}}, journal = {{IEEE Transactions on Software Engineering (TSE), presented at ICSE 2017}}, number = {{8}}, pages = {{739--759}}, publisher = {{IEEE}}, title = {{{Imprecise Matching of Requirements Specifications for Software Services using Fuzzy Logic}}}, doi = {{10.1109/TSE.2016.2632115}}, year = {{2016}}, } @inproceedings{184, abstract = {{In this paper, we propose a framework for a class of learning problems that we refer to as “learning to aggregate”. Roughly, learning-to-aggregate problems are supervised machine learning problems, in which instances are represented in the form of a composition of a (variable) number on constituents; such compositions are associated with an evaluation, score, or label, which is the target of the prediction task, and which can presumably be modeled in the form of a suitable aggregation of the properties of its constituents. Our learning-to-aggregate framework establishes a close connection between machine learning and a branch of mathematics devoted to the systematic study of aggregation functions. We specifically focus on a class of functions called uninorms, which combine conjunctive and disjunctive modes of aggregation. Experimental results for a corresponding model are presented for a review data set, for which the aggregation problem consists of combining different reviewer opinions about a paper into an overall decision of acceptance or rejection.}}, author = {{Melnikov, Vitaly and Hüllermeier, Eyke}}, booktitle = {{Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2016)}}, pages = {{756--771}}, title = {{{Learning to Aggregate Using Uninorms}}}, doi = {{10.1007/978-3-319-46227-1_47}}, year = {{2016}}, } @misc{10785, author = {{Fürnkranz, J. and Hüllermeier, Eyke}}, booktitle = {{Encyclopedia of Machine Learning and Data Mining}}, editor = {{Sammut, C. and Webb, G.I.}}, publisher = {{Springer}}, title = {{{Preference Learning}}}, year = {{2016}}, } @inproceedings{15400, author = {{Labreuche, C. and Hüllermeier, Eyke and Vojtas, P. and Fallah Tehrani, A.}}, booktitle = {{in Proceedings DA2PL 2016 EURO Mini Conference From Multiple Criteria Decision Aid to Preference Learning, Paderborn Germany}}, editor = {{Busa-Fekete, R. and Hüllermeier, Eyke and Mousseau, V. and Pfannschmidt, Karlson}}, title = {{{On the identifiability of models in multi-criteria preference learning}}}, year = {{2016}}, } @inproceedings{15401, author = {{Schäfer, D. and Hüllermeier, Eyke}}, booktitle = {{in Proceedings DA2PL`2016 Euro Mini Conference From Multiple Criteria Decision Aid to Preference Learning, Paderborn, Germany}}, editor = {{Busa-Fekete, R. and Hüllermeier, Eyke and Mousseau, V. and Pfannschmidt, Karlson}}, title = {{{Preference -based reinforcement learning using dyad ranking}}}, year = {{2016}}, } @inproceedings{15402, author = {{Couso, Ines and Ahmadi Fahandar, Mohsen and Hüllermeier, Eyke}}, booktitle = {{in Proceedings DA2PL 2016 EURO Mini Conference From Multiple Criteria Decision Aid to Preference Learning, Paderborn Germany}}, editor = {{Busa-Fekete, R. and Hüllermeier, Eyke and Mousseau, V. and Pfannschmidt, Karlson}}, title = {{{Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators}}}, year = {{2016}}, } @inproceedings{15403, author = {{Lu, S. and Hüllermeier, Eyke}}, booktitle = {{in Proceedings 26th Workshop Computational Intelligence, Dortmund Germany}}, editor = {{Hüllermeier, Eyke and Hoffmann, F. and Mikut, R.}}, pages = {{1--8}}, publisher = {{KIT Scientific Publishing}}, title = {{{Support vector classification on noisy data using fuzzy superset losses}}}, year = {{2016}}, } @inproceedings{15404, author = {{Schäfer, D. and Hüllermeier, Eyke}}, booktitle = {{in Workshop LWDA "Lernen, Wissen, Daten, Analysen" Potsdam, Germany}}, title = {{{Plackett-Luce networks for dyad ranking}}}, year = {{2016}}, } @inproceedings{15111, author = {{Pfannschmidt, Karlson and Hüllermeier, Eyke and Held, S. and Neiger, R.}}, booktitle = {{In Proceedings IPMU 16th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Part 1, Eindhoven, The Netherlands}}, pages = {{450--461}}, publisher = {{Springer}}, title = {{{Evaluating tests in medical diagnosis-Combining machine learning with game-theoretical concepts}}}, year = {{2016}}, } @article{16041, author = {{Leinweber, M. and Fober, T. and Strickert, M. and Baumgärtner, L. and Klebe, G. and Freisleben, B. and Hüllermeier, Eyke}}, journal = {{IEEE Transactions on Knowledge and Data Engineering}}, number = {{6}}, pages = {{1423--1434}}, title = {{{CavSimBase: A database for large scale comparison of protein binding sites}}}, volume = {{28}}, year = {{2016}}, } @phdthesis{141, author = {{Mohr, Felix}}, publisher = {{Universität Paderborn}}, title = {{{Towards Automated Service Composition Under Quality Constraints}}}, doi = {{10.17619/UNIPB/1-171}}, year = {{2016}}, } @inbook{10214, author = {{Fürnkranz, J. and Hüllermeier, Eyke}}, booktitle = {{Encyclopedia of Machine Learning and Data Mining}}, editor = {{Sammut, C. and Webb, G.I.}}, publisher = {{Springer}}, title = {{{Preference Learning}}}, year = {{2016}}, } @proceedings{10221, editor = {{Hoffmann, F. and Hüllermeier, Eyke and Mikut, R.}}, title = {{{ Proceedings 26. Workshop Computational Intelligence KIT Scientific Publishing, Karlsruhe, Germany}}}, year = {{2016}}, } @inproceedings{10222, author = {{Jasinska, K. and Dembczynski, K. and Busa-Fekete, Robert and Klerx, Timo and Hüllermeier, Eyke}}, booktitle = {{Proceedings ICML-2016, 33th International Conference on Machine Learning, New York, USA}}, editor = {{Balcan, M.F. and Weinberger, K.Q.}}, title = {{{Extreme F-measure maximization using sparse probability estimates }}}, year = {{2016}}, } @inproceedings{10223, author = {{Melnikov, Vitaly and Hüllermeier, Eyke}}, booktitle = {{European Conference on Machine Learning and Knowledge Discovery in Databases, Part II, Riva del Garda, Italy}}, pages = {{756--771}}, title = {{{Learning to aggregate using uninorms, in Proceedings ECML/PKDD-2016}}}, year = {{2016}}, } @inproceedings{10224, author = {{Dembczynski, K. and Kotlowski, W. and Waegeman, W. and Busa-Fekete, Robert and Hüllermeier, Eyke}}, booktitle = {{In Proceedings ECML/PKDD European Conference on Maschine Learning and Knowledge Discovery in Databases, Part II, Riva del Garda, Italy}}, pages = {{511--526}}, title = {{{Consistency of probalistic classifier trees}}}, year = {{2016}}, } @inproceedings{10225, author = {{Shabani, Aulon and Paul, Adil and Platon, R. and Hüllermeier, Eyke}}, booktitle = {{In Proceedings ICCBR, 24th International Conference on Case-Based Reasoning, Atlanta, GA, USA}}, pages = {{356--369}}, title = {{{Predicting the electricity consumption of buildings: An improved CBR approach}}}, year = {{2016}}, } @inproceedings{10226, author = {{Pfannschmidt, Karlson and Hüllermeier, Eyke and Held, S. and Neiger, R.}}, booktitle = {{In Proceedings IPMU 16th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Part 1, Eindhoven, The Netherlands}}, pages = {{450--461}}, publisher = {{Springer}}, title = {{{Evaluating tests in medical diagnosis-Combining machine learning with game-theoretical concepts}}}, year = {{2016}}, } @inproceedings{10227, author = {{Labreuche, C. and Hüllermeier, Eyke and Vojtas, P. and Fallah Tehrani, A.}}, booktitle = {{Proceedings DA2PL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning}}, editor = {{Busa-Fekete, Robert and Hüllermeier, Eyke and Mousseau, V. and Pfannschmidt, Karlson}}, title = {{{On the Identifiability of models in multi-criteria preference learning }}}, year = {{2016}}, } @inproceedings{10228, author = {{Schäfer, Dirk and Hüllermeier, Eyke}}, booktitle = {{Proceedings DA2PL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning}}, editor = {{Busa-Fekete, Robert and Hüllermeier, Eyke and Mousseau, V. and Pfannschmidt, Karlson}}, title = {{{Preference-Based Reinforcement Learning Using Dyad Ranking}}}, year = {{2016}}, } @inproceedings{10229, author = {{Couso, Ines and Ahmadi Fahandar, Mohsen and Hüllermeier, Eyke}}, booktitle = {{Proceedings DA2PL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning}}, editor = {{Busa-Fekete, Robert and Hüllermeier, Eyke and Mousseau, V. and Pfannschmidt, Karlson}}, title = {{{Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators}}}, year = {{2016}}, } @inproceedings{10230, author = {{Lu, S. and Hüllermeier, Eyke}}, booktitle = {{Proceedings 26. Workshop Computational Intelligence, KIT Scientific Publishing}}, editor = {{Hoffmann, F. and Hüllermeier, Eyke and Mikut, R.}}, pages = {{1--8}}, title = {{{Support vector classification on noisy data using fuzzy supersets losses}}}, year = {{2016}}, } @inproceedings{10231, author = {{Schäfer, Dirk and Hüllermeier, Eyke}}, booktitle = {{In Workshop LWDA "Lernen, Wissen, Daten, Analysen"}}, title = {{{Plackett-Luce networks for dyad ranking}}}, year = {{2016}}, } @proceedings{10263, editor = {{Kaminka, G.A. and Fox, M. and Bouquet, P. and Hüllermeier, Eyke and Dignum, V. and Dignum, F. and van Harmelen, F.}}, publisher = {{IOS Press}}, title = {{{ECAI 2016, 22nd European Conference on Artificial Intelligence, including PAIS 2016, Prestigious Applications of Artificial Intelligence}}}, volume = {{285}}, year = {{2016}}, } @article{10264, author = {{Leinweber, M. and Fober, T. and Strickert, M. and Baumgärtner, L. and Klebe, G. and Freisleben, B. and Hüllermeier, Eyke}}, journal = {{IEEE Transactions on Knowledge and Data Engineering}}, number = {{6}}, pages = {{1423--1434}}, title = {{{CavSimBase: A database for large scale comparison of protein binding sites}}}, volume = {{28}}, year = {{2016}}, } @article{10266, author = {{Riemenschneider, M. and Senge, Robin and Neumann, U. and Hüllermeier, Eyke and Heider, D.}}, journal = {{BioData Mining}}, number = {{10}}, title = {{{Exploiting HIV-1 protease and reverse transcriptase cross-resistance information for improved drug resistance prediction by means of multi-label classification}}}, volume = {{9}}, year = {{2016}}, } @inproceedings{280, abstract = {{The Collaborative Research Centre "On-The-Fly Computing" works on foundations and principles for the vision of the Future Internet. It proposes the paradigm of On-The-Fly Computing, which tackles emerging worldwide service markets. In these markets, service providers trade software, platform, and infrastructure as a service. Service requesters state requirements on services. To satisfy these requirements, the new role of brokers, who are (human) actors building service compositions on the fly, is introduced. Brokers have to specify service compositions formally and comprehensively using a domain-specific language (DSL), and to use service matching for the discovery of the constituent services available in the market. The broker's choice of the DSL and matching approaches influences her success of building compositions as distinctive properties of different service markets play a significant role. In this paper, we propose a new approach of engineering a situation-specific DSL by customizing a comprehensive, modular DSL and its matching for given service market properties. This enables the broker to create market-specific composition specifications and to perform market-specific service matching. As a result, the broker builds service compositions satisfying the requester's requirements more accurately. We evaluated the presented concepts using case studies in service markets for tourism and university management.}}, author = {{Arifulina, Svetlana and Platenius, Marie Christin and Mohr, Felix and Engels, Gregor and Schäfer, Wilhelm}}, booktitle = {{Proceedings of the IEEE 11th World Congress on Services (SERVICES), Visionary Track: Service Composition for the Future Internet}}, pages = {{333----340}}, title = {{{Market-Specific Service Compositions: Specification and Matching}}}, doi = {{10.1109/SERVICES.2015.58}}, year = {{2015}}, } @article{323, abstract = {{On-the-fly composition of service-based software solutions is still a challenging task. Even more challenges emerge when facing automatic service composition in markets of composed services for end users. In this paper, we focus on the functional discrepancy between “what a user wants” specified in terms of a request and “what a user gets” when executing a composed service. To meet the challenge of functional discrepancy, we propose the combination of existing symbolic composition approaches with machine learning techniques. We developed a learning recommendation system that expands the capabilities of existing composition algorithms to facilitate adaptivity and consequently reduces functional discrepancy. As a representative of symbolic techniques, an Artificial Intelligence planning based approach produces solutions that are correct with respect to formal specifications. Our learning recommendation system supports the symbolic approach in decision-making. Reinforcement Learning techniques enable the recommendation system to adjust its recommendation strategy over time based on user ratings. We implemented the proposed functionality in terms of a prototypical composition framework. Preliminary results from experiments conducted in the image processing domain illustrate the benefit of combining both complementary techniques.}}, author = {{Jungmann, Alexander and Mohr, Felix}}, journal = {{Journal of Internet Services and Applications}}, number = {{1}}, pages = {{1--18}}, publisher = {{Springer}}, title = {{{An approach towards adaptive service composition in markets of composed services}}}, doi = {{10.1186/s13174-015-0022-8}}, year = {{2015}}, } @inproceedings{324, abstract = {{Services are self-contained software components that can beused platform independent and that aim at maximizing software reuse. Abasic concern in service oriented architectures is to measure the reusabilityof services. One of the most important qualities is the functionalreusability, which indicates how relevant the task is that a service solves.Current metrics for functional reusability of software, however, have verylittle explanatory power and do not accomplish this goal.This paper presents a new approach to estimate the functional reusabilityof services based on their relevance. To this end, it denes the degreeto which a service enables the execution of other services as its contri-bution. Based on the contribution, relevance of services is dened as anestimation for their functional reusability. Explanatory power is obtainedby normalizing relevance values with a reference service. The applicationof the metric to a service test set conrms its supposed capabilities.}}, author = {{Mohr, Felix}}, booktitle = {{Proceedings of the 14th International Conference on Software Reuse (ICSR)}}, pages = {{298----313}}, title = {{{A Metric for Functional Reusability of Services}}}, doi = {{10.1007/978-3-319-14130-5_21}}, year = {{2015}}, } @inproceedings{319, abstract = {{Services are self-contained and platform independent software components that aim at maximizing software reuse. The automated composition of services to a target software artifact has been tackled with many AI techniques, but existing approaches make unreasonably strong assumptions such as a predefined data flow, are limited to tiny problem sizes, ignore non-functional properties, or assume offline service repositories. This paper presents an algorithm that automatically composes services without making such assumptions. We employ a backward search algorithm that starts from an empty composition and prepends service calls to already discovered candidates until a solution is found. Available services are determined during the search process. We implemented our algorithm, performed an experimental evaluation, and compared it to other approaches.}}, author = {{Mohr, Felix and Jungmann, Alexander and Kleine Büning, Hans}}, booktitle = {{Proceedings of the 12th IEEE International Conference on Services Computing (SCC)}}, pages = {{57----64}}, title = {{{Automated Online Service Composition}}}, doi = {{10.1109/SCC.2015.18}}, year = {{2015}}, } @article{4792, author = {{Senge, Robin and Hüllermeier, Eyke}}, issn = {{1063-6706}}, journal = {{IEEE Transactions on Fuzzy Systems}}, number = {{6}}, pages = {{2024--2033}}, publisher = {{Institute of Electrical and Electronics Engineers (IEEE)}}, title = {{{Fast Fuzzy Pattern Tree Learning for Classification}}}, doi = {{10.1109/tfuzz.2015.2396078}}, volume = {{23}}, year = {{2015}}, } @inproceedings{15406, author = {{Schäfer, D. and Hüllermeier, Eyke}}, booktitle = {{in Proceedings of the 2015 international Workshop on Meta-Learning and Algorithm Selection co-located ECML/PKDD, Porto, Portugal}}, pages = {{110--111}}, title = {{{Preference-based meta-learning using dyad ranking: Recommending algorithms in cold-start situations}}}, year = {{2015}}, } @inproceedings{15749, author = {{Paul, Adil and Hüllermeier, Eyke}}, booktitle = {{In Workshop Proceedings from ICCBR, 23rd International Conference on Case-Based Reasoning, Frankfurt, Germany}}, pages = {{68--77}}, title = {{{A cbr approach to the angry birds game}}}, year = {{2015}}, } @inproceedings{15750, author = {{Ewerth, R. and Balz, A. and Gehlhaar, J. and Dembczynski, K. and Hüllermeier, Eyke}}, booktitle = {{In Proceedings 25. Workshop Computational Intelligence, Dortmund, Germany}}, editor = {{Hoffmann, F. and Hüllermeier, Eyke}}, pages = {{235--240}}, publisher = {{KIT Scientific Publishing}}, title = {{{Depth estimation in monocular images: Quantitative versus qualitative approaches}}}, year = {{2015}}, } @inproceedings{15751, author = {{Lu, S. and Hüllermeier, Eyke}}, booktitle = {{in Proceedings 25th Workshop Computational Intelligence, Dortmund Germany}}, editor = {{Hoffmann, F. and Hüllermeier, Eyke}}, pages = {{97--104}}, publisher = {{KIT Scientific Publishing}}, title = {{{Locally weighted regression through data imprecisiation}}}, year = {{2015}}, } @article{16049, author = {{Senge, Robin and Hüllermeier, Eyke}}, journal = {{IEEE Transactions on Fuzzy Systems}}, number = {{6}}, pages = {{2024--2033}}, title = {{{Fast fuzzy pattern tree learning for classification }}}, volume = {{23}}, year = {{2015}}, } @article{16051, author = {{Hüllermeier, Eyke}}, journal = {{Informatik Spektrum}}, number = {{6}}, pages = {{500--509}}, title = {{{From knowledge-based to data driven fuzzy modeling: Development, criticism and alternative directions}}}, volume = {{38}}, year = {{2015}}, } @article{16053, author = {{Hüllermeier, Eyke}}, journal = {{Fuzzy Sets and Systems}}, pages = {{292--299}}, title = {{{Does machine learning need fuzzy logic?}}}, volume = {{281}}, year = {{2015}}, } @article{16058, author = {{Waegeman, W. and Dembczynski, K. and Jachnik, A. and Cheng, W. and Hüllermeier, Eyke}}, journal = {{Journal of Machine Learning Research}}, pages = {{3313--3368}}, title = {{{On the Bayes-optimality of F-measure maximizers}}}, volume = {{15}}, year = {{2015}}, } @article{16067, author = {{Shaker, A. and Hüllermeier, Eyke}}, journal = {{Neurocomputing}}, pages = {{250--264}}, title = {{{Recovery analysis for adaptive learning from non-stationary data streams: Experimental design and case study}}}, volume = {{150}}, year = {{2015}}, } @inproceedings{10234, author = {{Hüllermeier, Eyke and Minor, M.}}, booktitle = {{in Proceedings 23rd International Conference on Case-Based Reasoning (ICCBR 2015) LNAI 9343}}, publisher = {{Springer}}, title = {{{Case-Based Reasoning Research and Development }}}, year = {{2015}}, } @inproceedings{10235, author = {{Hoffmann, F. and Hüllermeier, Eyke}}, title = {{{Proceedings 25. Workshop Computational Intelligence KIT Scientific Publishing}}}, year = {{2015}}, } @inproceedings{10236, author = {{Abdel-Aziz, A. and Hüllermeier, Eyke}}, booktitle = {{In Proceedings 23rd International Conference on Case-Based Reasoning (ICCBR 2015)}}, pages = {{1--14}}, title = {{{Case Base Maintenance in Preference-Based CBR}}}, year = {{2015}}, } @inproceedings{10237, author = {{Szörényi, B. and Busa-Fekete, Robert and Weng, P. and Hüllermeier, Eyke}}, booktitle = {{In Proceedings International Conference on Machine Learning (ICML 2015)}}, pages = {{1660--1668}}, title = {{{Qualitative Multi-Armed Bandits: A Quantile-Based Approach}}}, year = {{2015}}, } @inproceedings{10238, author = {{Schäfer, Dirk and Hüllermeier, Eyke}}, booktitle = {{in Proceedings European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD)}}, pages = {{227--242}}, title = {{{Dyad Ranking Using A Bilinear Plackett-Luce Model}}}, year = {{2015}}, } @inproceedings{10239, author = {{Hüllermeier, Eyke and Cheng, W.}}, booktitle = {{in Proceedings European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD)}}, pages = {{260--275}}, title = {{{Superset Learning Based on Generalized Loss Minimization }}}, year = {{2015}}, } @inproceedings{10240, author = {{Henzgen, Sascha and Hüllermeier, Eyke}}, booktitle = {{in Proceedings European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD)}}, pages = {{422--437}}, title = {{{Weighted Rank Correlation : A Flexible Approach Based on Fuzzy Order Relations}}}, year = {{2015}}, } @inproceedings{10241, author = {{Szörényi, B. and Busa-Fekete, Robert and Paul, Adil and Hüllermeier, Eyke}}, booktitle = {{in Advances in Neural Information Processing Systems 28 (NIPS 2015)}}, pages = {{604--612}}, title = {{{Online Rank Elicitation for Plackett-Luce: A Dueling Bandits Approach}}}, year = {{2015}}, } @inproceedings{10242, author = {{Szörényi, B. and Busa-Fekete, Robert and Dembczynski, K. and Hüllermeier, Eyke}}, booktitle = {{in Advances in Neural Information Processing Systems 28 (NIPS 2015)}}, pages = {{595--603}}, title = {{{Online F-Measure Optimization}}}, year = {{2015}}, } @inproceedings{10243, author = {{El Mesaoudi-Paul, Adil and Hüllermeier, Eyke}}, booktitle = {{in Workshop Proc. 23rd International Conference on Case-Based Reasoning (ICCBR 2015)}}, pages = {{68--77}}, title = {{{A CBR Approach to the Angry Birds Game}}}, year = {{2015}}, } @inproceedings{10244, author = {{Schäfer, Dirk and Hüllermeier, Eyke}}, booktitle = {{in Proceedings of the 2015 International Workshop on Meta-Learning and Algorithm Selection (MetaSel@PKDD/ECML)}}, pages = {{110--111}}, title = {{{Preference-Based Meta- Learning Using Dyad Ranking: Recommending Algorithms in Cold-Start Situations}}}, year = {{2015}}, } @inproceedings{10245, author = {{Lu, S. and Hüllermeier, Eyke}}, booktitle = {{Proceedings 25. Workshop Computational Intelligence}}, pages = {{97--104}}, title = {{{Locally weighted regression through data imprecisiation}}}, year = {{2015}}, } @inproceedings{10246, author = {{Ewerth, Ralph and Balz, A. and Gehlhaar, J. and Dembczynski, K. and Hüllermeier, Eyke}}, booktitle = {{Proceedings 25. Workshop Computational Intelligence}}, pages = {{235--240}}, title = {{{Depth estimation in monocular images: Quantitative versus qualitative approaches}}}, year = {{2015}}, } @article{10319, author = {{Waegeman, W. and Dembczynski, K. and Jachnik, A. and Cheng, W. and Hüllermeier, Eyke}}, journal = {{in Journal of Machine Learning Research}}, pages = {{3333--3388}}, title = {{{On the Bayes-Optimality of F-Measure Maximizers}}}, volume = {{15}}, year = {{2015}}, } @article{10320, author = {{Hüllermeier, Eyke}}, journal = {{Fuzzy Sets and Systems}}, pages = {{292--299}}, title = {{{Does machine learning need fuzzy logic?}}}, volume = {{281}}, year = {{2015}}, } @article{10321, author = {{Shaker, Ammar and Hüllermeier, Eyke}}, journal = {{Neurocomputing}}, pages = {{250--264}}, title = {{{Recovery analysis for adaptive learning from non-stationary data streams: Experimental design and case study}}}, volume = {{150}}, year = {{2015}}, } @article{10322, author = {{Hüllermeier, Eyke}}, journal = {{Informatik Spektrum}}, number = {{6}}, pages = {{500--509}}, title = {{{From Knowledge-based to Data-driven fuzzy modeling-Development, criticism and alternative directions}}}, volume = {{38}}, year = {{2015}}, } @article{10323, author = {{Garcia-Jimenez, S. and Bustince, U. and Hüllermeier, Eyke and Mesiar, R. and Pal, N.R. and Pradera, A.}}, journal = {{IEEE Transactions on Fuzzy Systems}}, number = {{4}}, pages = {{1259--1273}}, title = {{{Overlap Indices: Construction of and Application of Interpolative Fuzzy Systems}}}, volume = {{23}}, year = {{2015}}, } @article{10324, author = {{Senge, Robin and Hüllermeier, Eyke}}, journal = {{IEEE Transactions on Fuzzy Systems}}, number = {{6}}, pages = {{2024--2033}}, title = {{{Fast Fuzzy Pattern Tree Learning of Classification}}}, volume = {{23}}, year = {{2015}}, } @article{24155, author = {{Basavaraju, Manu and Chandran, L Sunil and Rajendraprasad, Deepak and Ramaswamy, Arunselvan}}, journal = {{Graphs and Combinatorics}}, number = {{6}}, pages = {{1363--1382}}, publisher = {{Springer}}, title = {{{Rainbow connection number of graph power and graph products}}}, volume = {{30}}, year = {{2014}}, } @article{24156, author = {{Basavaraju, Manu and Chandran, L Sunil and Rajendraprasad, Deepak and Ramaswamy, Arunselvan}}, journal = {{Graphs and Combinatorics}}, number = {{2}}, pages = {{275--285}}, publisher = {{Springer}}, title = {{{Rainbow connection number and radius}}}, volume = {{30}}, year = {{2014}}, } @inproceedings{353, abstract = {{There are many technologies for the automation of processesthat deal with services; examples are service discovery and composition.Automation of these processes requires that the services are described semantically. However, semantically described services are currently not oronly rarely available, which limits the applicability of discovery and composition approaches. The systematic support for creating new semanticservices usable by automated technologies is an open problem.We tackle this problem with a template based approach: Domain independent templates are instantiated with domain specific services andboolean expressions. The obtained services have semantic descriptionswhose correctness directly follows from the correctness of the template.Besides the theory, we present experimental results for a service repository in which 85% of the services were generated automatically.}}, author = {{Mohr, Felix and Walther, Sven}}, booktitle = {{Proceedings of the 14th International Conference on Software Reuse (ICSR)}}, pages = {{188--203}}, title = {{{Template-based Generation of Semantic Services}}}, doi = {{10.1007/978-3-319-14130-5_14}}, year = {{2014}}, } @inproceedings{447, abstract = {{Automatic service composition is still a challengingtask. It is even more challenging when dealing witha dynamic market of services for end users. New servicesmay enter the market while other services are completelyremoved. Furthermore, end users are typically no experts in thedomain in which they formulate a request. As a consequence,ambiguous user requests will inevitably emerge and have tobe taken into account. To meet these challenges, we proposea new approach that combines automatic service compositionwith adaptive service recommendation. A best first backwardsearch algorithm produces solutions that are functional correctwith respect to user requests. An adaptive recommendationsystem supports the search algorithm in decision-making.Reinforcement Learning techniques enable the system to adjustits recommendation strategy over time based on user ratings.The integrated approach is described on a conceptional leveland demonstrated by means of an illustrative example fromthe image processing domain.}}, author = {{Jungmann, Alexander and Mohr, Felix and Kleinjohann, Bernd}}, booktitle = {{Proceedings of the 10th World Congress on Services (SERVICES)}}, pages = {{346--353}}, title = {{{Combining Automatic Service Composition with Adaptive Service Recommendation for Dynamic Markets of Services}}}, doi = {{10.1109/SERVICES.2014.68}}, year = {{2014}}, } @inproceedings{457, abstract = {{Automatically composing service-based software solutionsis still a challenging task. Functional as well as nonfunctionalproperties have to be considered in order to satisfyindividual user requests. Regarding non-functional properties,the composition process can be modeled as optimization problemand solved accordingly. Functional properties, in turn, can bedescribed by means of a formal specification language. Statespacebased planning approaches can then be applied to solvethe underlying composition problem. However, depending on theexpressiveness of the applied formalism and the completenessof the functional descriptions, formally equivalent services maystill differ with respect to their implemented functionality. As aconsequence, the most appropriate solution for a desired functionalitycan hardly be determined without considering additionalinformation. In this paper, we demonstrate how to overcome thislack of information by means of Reinforcement Learning. Inorder to resolve ambiguity, we expand state-space based servicecomposition by a recommendation mechanism that supportsdecision-making beyond formal specifications. The recommendationmechanism adjusts its recommendation strategy basedon feedback from previous composition runs. Image processingserves as case study. Experimental results show the benefit of ourproposed solution.}}, author = {{Jungmann, Alexander and Mohr, Felix and Kleinjohann, Bernd }}, booktitle = {{Proceedings of the 7th International Conference on Service Oriented Computing and Applications (SOCA)}}, pages = {{105--112}}, title = {{{Applying Reinforcement Learning for Resolving Ambiguity in Service Composition}}}, doi = {{10.1109/SOCA.2014.48}}, year = {{2014}}, } @inproceedings{428, abstract = {{Services are self-contained software components that can be used platform independent and that aim at maximizing software reuse. A basic concern in service oriented architectures is to measure the reusability of services. One of the most important qualities is the functional reusability, which indicates how relevant the task is that a service solves. Current metrics for functional reusability of software, however, either require source code analysis or have very little explanatory power. This paper gives a formally described vision statement for the estimation of functional reusability of services and sketches an exemplary reusability metric that is based on the service descriptions.}}, author = {{Mohr, Felix}}, booktitle = {{Proceedings of the 12th International Conference on Service Oriented Computing (ICSOC)}}, pages = {{411--418}}, title = {{{Estimating Functional Reusability of Services}}}, year = {{2014}}, } @article{16046, author = {{Agarwal, M. and Fallah Tehrani, A. and Hüllermeier, Eyke}}, journal = {{Journal of Multi-Criteria Decision Analysis}}, number = {{3-4}}, title = {{{Preference-based learning of ideal solutions in TOPSIS-like decision models}}}, volume = {{22}}, year = {{2014}}, } @article{16060, author = {{Krotzky, T. and Fober, T. and Hüllermeier, Eyke and Klebe, G.}}, journal = {{IEEE/ACM Transactions of Computational Biology and Bioinformatics}}, number = {{5}}, pages = {{878--890}}, title = {{{Extended graph-based models for enhanced similarity search in Cabase}}}, volume = {{11}}, year = {{2014}}, } @article{16064, author = {{Hüllermeier, Eyke}}, journal = {{International Journal of Approximate Reasoning}}, number = {{7}}, pages = {{1519--1534}}, title = {{{Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization}}}, volume = {{55}}, year = {{2014}}, } @article{16069, author = {{Henzgen, Sascha and Strickert, M. and Hüllermeier, Eyke}}, journal = {{Evolving Systems}}, pages = {{175--191}}, title = {{{Visualization of evolving fuzzy-rule-based systems}}}, volume = {{5}}, year = {{2014}}, } @article{16077, author = {{Busa-Fekete, Robert and Szörenyi, B. and Weng, P. and Cheng, W. and Hüllermeier, Eyke}}, journal = {{Machine Learning}}, number = {{3}}, pages = {{327--351}}, title = {{{Preference-based reinforcement learning: evolutionary direct policy search using a preference-based racing algorithm.}}}, volume = {{97}}, year = {{2014}}, } @article{16078, author = {{Krempl, G. and Zliobaite, I. and Brzezinski, D. and Hüllermeier, Eyke and Last, M. and Lemaire, V. and Noack, T. and Shaker, A. and Sievi, S. and Spiliopoulou, M. and Stefanowski, J.}}, journal = {{SIGKDD Explorations}}, number = {{1}}, pages = {{1--10}}, title = {{{Open challenges for data stream mining research}}}, volume = {{16}}, year = {{2014}}, } @article{16079, author = {{Strickert, M. and Bunte, K. and Schleif, F.M. and Hüllermeier, Eyke}}, journal = {{Neurocomputing}}, pages = {{97--109}}, title = {{{Correlation-based embedding of pairwise score data}}}, volume = {{141}}, year = {{2014}}, } @article{16080, author = {{Shaker, Ammar and Hüllermeier, Eyke}}, journal = {{International Journal of Applied Mathematics and Computer Science}}, number = {{1}}, pages = {{199--212}}, title = {{{Survival analysis on data streams: Analyzing temporal events in dynamically changing environments}}}, volume = {{24}}, year = {{2014}}, }