TY - CONF AB - 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. AU - Jakobs, Marie-Christine AU - Krämer, Julia AU - van Straaten, Dirk AU - Lettmann, Theodor ED - Marcelo De Barros, Janusz Klink,Tadeus Uhl, Thomas Prinz ID - 115 T2 - The Ninth International Conferences on Advanced Service Computing (SERVICE COMPUTATION) TI - Certification Matters for Service Markets ER - TY - CONF AB - 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. AU - Seemann, Nina AU - Merten, Marie-Luis AU - Geierhos, Michaela AU - Tophinke, Doris AU - Hüllermeier, Eyke ID - 1158 T2 - Proceedings of the Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature TI - Annotation Challenges for Reconstructing the Structural Elaboration of Middle Low German ER - TY - GEN AU - Schnitker, Nino Noel ID - 5694 TI - Genetischer Algorithmus zur Erstellung von Ensembles von Nested Dichotomies ER - TY - GEN AU - Gupta, Pritha AU - Hetzer, Alexander AU - Tornede, Tanja AU - Gottschalk, Sebastian AU - Kornelsen, Andreas AU - Osterbrink, Sebastian AU - Pfannschmidt, Karlson AU - Hüllermeier, Eyke ID - 5722 TI - jPL: A Java-based Software Framework for Preference Learning ER - TY - GEN AU - Hetzer, Alexander AU - Tornede, Tanja ID - 5724 TI - Solving the Container Pre-Marshalling Problem using Reinforcement Learning and Structured Output Prediction ER - TY - CONF AB - 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). AU - Czech, Mike AU - Hüllermeier, Eyke AU - Jakobs, Marie-Christine AU - Wehrheim, Heike ID - 71 T2 - Proceedings of the 3rd International Workshop on Software Analytics TI - Predicting Rankings of Software Verification Tools ER - TY - GEN AB - 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). AU - Czech, Mike AU - Hüllermeier, Eyke AU - Jakobs, Marie-Christine AU - Wehrheim, Heike ID - 72 TI - Predicting Rankings of Software Verification Competitions ER - TY - GEN AU - Fürnkranz, J. AU - Hüllermeier, Eyke ID - 10589 T2 - Encyclopedia of Machine Learning and Data Mining TI - Preference Learning ER - TY - CHAP AU - Fürnkranz, J. AU - Hüllermeier, Eyke ED - Sammut, C. ED - Webb, G.I. ID - 10784 T2 - Encyclopedia of Machine Learning and Data Mining TI - Preference Learning VL - 107 ER - TY - CONF AB - 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. AU - Wever, Marcel Dominik AU - Mohr, Felix AU - Hüllermeier, Eyke ID - 1180 T2 - 27th Workshop Computational Intelligence TI - Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization ER - TY - CONF AU - Melnikov, Vitaly AU - Hüllermeier, Eyke ED - Hoffmann, F. ED - Hüllermeier, Eyke ED - Mikut, R. ID - 15397 T2 - in Proceedings 27th Workshop Computational Intelligence, Dortmund Germany TI - Optimizing the structure of nested dichotomies. A comparison of two heuristics ER - TY - CONF AU - Czech, M. AU - Hüllermeier, Eyke AU - Jacobs, M.C. AU - Wehrheim, Heike ID - 15399 T2 - in Proceedings ESEC/FSE Workshops 2017 - 3rd ACM SIGSOFT, International Workshop on Software Analytics (SWAN 2017), Paderborn Germany TI - Predicting rankings of software verification tools ER - TY - CONF AU - Couso, Ines AU - Dubois, D. AU - Hüllermeier, Eyke ID - 15110 T2 - in Proceedings SUM 2017, 11th International Conference on Scalable Uncertainty Management, Granada, Spain TI - Maximum likelihood estimation and coarse data ER - TY - CONF AU - Ewerth, Ralph AU - Springstein, M. AU - Müller, E. AU - Balz, A. AU - Gehlhaar, J. AU - Naziyok, T. AU - Dembczynski, K. AU - Hüllermeier, Eyke ID - 10204 T2 - Proc. IEEE Int. Conf. on Multimedia and Expo (ICME 2017) TI - Estimating relative depth in single images via rankboost ER - TY - CONF AU - Ahmadi Fahandar, Mohsen AU - Hüllermeier, Eyke AU - Couso, Ines ID - 10205 T2 - Proc. 34th Int. Conf. on Machine Learning (ICML 2017) TI - Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent Coarsening ER - TY - CONF AU - Mohr, Felix AU - Lettmann, Theodor AU - Hüllermeier, Eyke ID - 10206 T2 - Proc. 40th Annual German Conference on Advances in Artificial Intelligence (KI 2017) TI - Planning with Independent Task Networks ER - TY - CONF AU - Czech, M. AU - Hüllermeier, Eyke AU - Jakobs, M.-C. AU - Wehrheim, Heike ID - 10207 T2 - Proc. 3rd ACM SIGSOFT Int. I Workshop on Software Analytics (SWAN@ESEC/SIGSOFT FSE 2017 TI - Predicting rankings of software verification tools ER - TY - CONF AU - Couso, Ines AU - Dubois, D. AU - Hüllermeier, Eyke ID - 10208 T2 - Proc. 11th Int. Conf. on Scalable Uncertainty Management (SUM 2017) TI - Maximum Likelihood Estimation and Coarse Data ER - TY - CONF AU - Ahmadi Fahandar, Mohsen AU - Hüllermeier, Eyke ID - 10209 T2 - Proc. AAAI 2017, 32nd AAAI Conference on Artificial Intelligence TI - Learning to Rank based on Analogical Reasoning ER - TY - CONF AU - Hoffmann, F. AU - Hüllermeier, Eyke AU - Mikut, R. ID - 10212 TI - (Hrsg.) Proceedings 27. Workshop Computational Intelligence, KIT Scientific Publishing, Karlsruhe, Germany 2017 ER - TY - CONF AU - Melnikov, Vitaly AU - Hüllermeier, Eyke ID - 10213 T2 - Proceedings 27. Workshop Computational Intelligence, Dortmund, Germany 2017 TI - Optimizing the Structure of Nested Dichotomies: A Comparison of Two Heuristics ER - TY - CONF AU - Shaker, Ammar AU - Heldt, W. AU - Hüllermeier, Eyke ID - 10216 T2 - Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases, Skopje, Macedonia TI - Learning TSK Fuzzy Rules from Data Streams ER - TY - JOUR AU - Bräuning, M. AU - Hüllermeier, Eyke AU - Keller, T. AU - Glaum, M. ID - 10267 IS - 1 JF - European Journal of Operational Research TI - Lexicographic preferences for predictive modeling of human decision making. A new machine learning method with an application in accounting VL - 258 ER - TY - JOUR AU - Platenius, M.-C. AU - Shaker, Ammar AU - Becker, M. AU - Hüllermeier, Eyke AU - Schäfer, W. ID - 10268 IS - 8 JF - IEEE Transactions on Software Engineering TI - Imprecise Matching of Requirements Specifications for Software Services Using Fuzzy Logic VL - 43 ER - TY - JOUR AU - Hüllermeier, Eyke ID - 10269 JF - The Computing Research Repository (CoRR) TI - From Knowledge-based to Data-driven Modeling of Fuzzy Rule-based Systems: A Critical Reflection ER - TY - JOUR AU - Ramaswamy, Arunselvan AU - Bhatnagar, Shalabh ID - 24154 IS - 8 JF - Stochastics TI - Stochastic recursive inclusion in two timescales with an application to the lagrangian dual problem VL - 88 ER - TY - JOUR AU - Melnikov, Vitalik AU - Hüllermeier, Eyke AU - Kaimann, Daniel AU - Frick, Bernd AU - Gupta, Pritha ID - 3318 JF - Schedae Informaticae SN - 2083-8476 TI - Pairwise versus Pointwise Ranking: A Case Study VL - 25 ER - TY - JOUR AB - 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. AU - Platenius, Marie Christin AU - Shaker, Ammar AU - Becker, Matthias AU - Hüllermeier, Eyke AU - Schäfer, Wilhelm ID - 190 IS - 8 JF - IEEE Transactions on Software Engineering (TSE), presented at ICSE 2017 TI - Imprecise Matching of Requirements Specifications for Software Services using Fuzzy Logic ER - TY - CONF AB - 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. AU - Melnikov, Vitaly AU - Hüllermeier, Eyke ID - 184 T2 - Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2016) TI - Learning to Aggregate Using Uninorms ER - TY - GEN AU - Fürnkranz, J. AU - Hüllermeier, Eyke ED - Sammut, C. ED - Webb, G.I. ID - 10785 T2 - Encyclopedia of Machine Learning and Data Mining TI - Preference Learning ER - TY - CONF AU - Labreuche, C. AU - Hüllermeier, Eyke AU - Vojtas, P. AU - Fallah Tehrani, A. ED - Busa-Fekete, R. ED - Hüllermeier, Eyke ED - Mousseau, V. ED - Pfannschmidt, Karlson ID - 15400 T2 - in Proceedings DA2PL 2016 EURO Mini Conference From Multiple Criteria Decision Aid to Preference Learning, Paderborn Germany TI - On the identifiability of models in multi-criteria preference learning ER - TY - CONF AU - Schäfer, D. AU - Hüllermeier, Eyke ED - Busa-Fekete, R. ED - Hüllermeier, Eyke ED - Mousseau, V. ED - Pfannschmidt, Karlson ID - 15401 T2 - in Proceedings DA2PL`2016 Euro Mini Conference From Multiple Criteria Decision Aid to Preference Learning, Paderborn, Germany TI - Preference -based reinforcement learning using dyad ranking ER - TY - CONF AU - Couso, Ines AU - Ahmadi Fahandar, Mohsen AU - Hüllermeier, Eyke ED - Busa-Fekete, R. ED - Hüllermeier, Eyke ED - Mousseau, V. ED - Pfannschmidt, Karlson ID - 15402 T2 - in Proceedings DA2PL 2016 EURO Mini Conference From Multiple Criteria Decision Aid to Preference Learning, Paderborn Germany TI - Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators ER - TY - CONF AU - Lu, S. AU - Hüllermeier, Eyke ED - Hüllermeier, Eyke ED - Hoffmann, F. ED - Mikut, R. ID - 15403 T2 - in Proceedings 26th Workshop Computational Intelligence, Dortmund Germany TI - Support vector classification on noisy data using fuzzy superset losses ER - TY - CONF AU - Schäfer, D. AU - Hüllermeier, Eyke ID - 15404 T2 - in Workshop LWDA "Lernen, Wissen, Daten, Analysen" Potsdam, Germany TI - Plackett-Luce networks for dyad ranking ER - TY - CONF AU - Pfannschmidt, Karlson AU - Hüllermeier, Eyke AU - Held, S. AU - Neiger, R. ID - 15111 T2 - In Proceedings IPMU 16th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Part 1, Eindhoven, The Netherlands TI - Evaluating tests in medical diagnosis-Combining machine learning with game-theoretical concepts ER - TY - JOUR AU - Leinweber, M. AU - Fober, T. AU - Strickert, M. AU - Baumgärtner, L. AU - Klebe, G. AU - Freisleben, B. AU - Hüllermeier, Eyke ID - 16041 IS - 6 JF - IEEE Transactions on Knowledge and Data Engineering TI - CavSimBase: A database for large scale comparison of protein binding sites VL - 28 ER - TY - THES AU - Mohr, Felix ID - 141 TI - Towards Automated Service Composition Under Quality Constraints ER - TY - CHAP AU - Fürnkranz, J. AU - Hüllermeier, Eyke ED - Sammut, C. ED - Webb, G.I. ID - 10214 T2 - Encyclopedia of Machine Learning and Data Mining TI - Preference Learning ER - TY - GEN ED - Hoffmann, F. ED - Hüllermeier, Eyke ED - Mikut, R. ID - 10221 TI - Proceedings 26. Workshop Computational Intelligence KIT Scientific Publishing, Karlsruhe, Germany ER - TY - CONF AU - Jasinska, K. AU - Dembczynski, K. AU - Busa-Fekete, Robert AU - Klerx, Timo AU - Hüllermeier, Eyke ED - Balcan, M.F. ED - Weinberger, K.Q. ID - 10222 T2 - Proceedings ICML-2016, 33th International Conference on Machine Learning, New York, USA TI - Extreme F-measure maximization using sparse probability estimates ER - TY - CONF AU - Melnikov, Vitaly AU - Hüllermeier, Eyke ID - 10223 T2 - European Conference on Machine Learning and Knowledge Discovery in Databases, Part II, Riva del Garda, Italy TI - Learning to aggregate using uninorms, in Proceedings ECML/PKDD-2016 ER - TY - CONF AU - Dembczynski, K. AU - Kotlowski, W. AU - Waegeman, W. AU - Busa-Fekete, Robert AU - Hüllermeier, Eyke ID - 10224 T2 - In Proceedings ECML/PKDD European Conference on Maschine Learning and Knowledge Discovery in Databases, Part II, Riva del Garda, Italy TI - Consistency of probalistic classifier trees ER - TY - CONF AU - Shabani, Aulon AU - Paul, Adil AU - Platon, R. AU - Hüllermeier, Eyke ID - 10225 T2 - In Proceedings ICCBR, 24th International Conference on Case-Based Reasoning, Atlanta, GA, USA TI - Predicting the electricity consumption of buildings: An improved CBR approach ER - TY - CONF AU - Pfannschmidt, Karlson AU - Hüllermeier, Eyke AU - Held, S. AU - Neiger, R. ID - 10226 T2 - In Proceedings IPMU 16th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Part 1, Eindhoven, The Netherlands TI - Evaluating tests in medical diagnosis-Combining machine learning with game-theoretical concepts ER - TY - CONF AU - Labreuche, C. AU - Hüllermeier, Eyke AU - Vojtas, P. AU - Fallah Tehrani, A. ED - Busa-Fekete, Robert ED - Hüllermeier, Eyke ED - Mousseau, V. ED - Pfannschmidt, Karlson ID - 10227 T2 - Proceedings DA2PL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning TI - On the Identifiability of models in multi-criteria preference learning ER - TY - CONF AU - Schäfer, Dirk AU - Hüllermeier, Eyke ED - Busa-Fekete, Robert ED - Hüllermeier, Eyke ED - Mousseau, V. ED - Pfannschmidt, Karlson ID - 10228 T2 - Proceedings DA2PL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning TI - Preference-Based Reinforcement Learning Using Dyad Ranking ER - TY - CONF AU - Couso, Ines AU - Ahmadi Fahandar, Mohsen AU - Hüllermeier, Eyke ED - Busa-Fekete, Robert ED - Hüllermeier, Eyke ED - Mousseau, V. ED - Pfannschmidt, Karlson ID - 10229 T2 - Proceedings DA2PL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning TI - Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators ER - TY - CONF AU - Lu, S. AU - Hüllermeier, Eyke ED - Hoffmann, F. ED - Hüllermeier, Eyke ED - Mikut, R. ID - 10230 T2 - Proceedings 26. Workshop Computational Intelligence, KIT Scientific Publishing TI - Support vector classification on noisy data using fuzzy supersets losses ER - TY - CONF AU - Schäfer, Dirk AU - Hüllermeier, Eyke ID - 10231 T2 - In Workshop LWDA "Lernen, Wissen, Daten, Analysen" TI - Plackett-Luce networks for dyad ranking ER -