Uniform approximation in classical weak convergence theory - Bengs, Viktor; Holzmann, Hajo (arXiv:1903.09864)
A common statistical task lies in showing asymptotic normality of certain statistics. In many of these situations, classical textbook results on weak convergence theory suffice for the problem at hand. However, there are quite some scenarios where stronger results are needed in order to establish an asymptotic normal approximation uniformly over a family of probability measures. In this note we collect some results in this direction. We restrict ourselves to weak convergence in $\mathbb R^d$ with continuous limit measures.

Towards Automated Machine Learning for Multi-Label Classification - Wever, Marcel Dominik; Mohr, Felix; Hüllermeier, Eyke; Hetzer, Alexander

Choice Functions Generated by Mallows and Plackett–Luce Relations - Tagne, V. K.; Fotso, S.; Fono, L. A.; Hüllermeier, Eyke (New Mathematics and Natural Computation)

Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning - Couso, Ines; Borgelt, Christian; Hüllermeier, Eyke; Kruse, Rudolf (IEEE Computational Intelligence Magazine)

Multi-target prediction: a unifying view on problems and methods - Waegeman, Willem; Dembczynski, Krzysztof; Hüllermeier, Eyke (Data Mining and Knowledge Discovery)
Many problem settings in machine learning are concerned with the simultaneous prediction of multiple target variables of diverse type. Amongst others, such problem settings arise in multivariate regression, multi-label classification, multi-task learning, dyadic prediction, zero-shot learning, network inference, and matrix completion. These subfields of machine learning are typically studied in isolation, without highlighting or exploring important relationships. In this paper, we present a unifying view on what we call multi-target prediction (MTP) problems and methods. First, we formally discuss commonalities and differences between existing MTP problems. To this end, we introduce a general framework that covers the above subfields as special cases. As a second contribution, we provide a structured overview of MTP methods. This is accomplished by identifying a number of key properties, which distinguish such methods and determine their suitability for different types of problems. Finally, we also discuss a few challenges for future research.

Set-Valued Prediction in Multi-Class Classification - Mortier, Thomas; Wydmuch, Marek; Dembczynski, Krzysztof; Hüllermeier, Eyke; Waegeman, Willem (Proceedings of the 31st Benelux Conference on Artificial Intelligence {(BNAIC} 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), Brussels, Belgium, November 6-8, 2019)

Feature Selection for Analogy-Based Learning to Rank - Ahmadi Fahandar, Mohsen; Hüllermeier, Eyke (Discovery Science)

Analogy-Based Preference Learning with Kernels - Ahmadi Fahandar, Mohsen; Hüllermeier, Eyke (KI 2019: Advances in Artificial Intelligence)

Epistemic Uncertainty Sampling - Nguyen, Vu-Linh; Destercke, Sébastien; Hüllermeier, Eyke (Discovery Science)

Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA - Melnikov, Vitaly; Hüllermeier, Eyke (Proceedings ACML, Asian Conference on Machine Learning (Proceedings of Machine Learning Research, 101))

Influence of Cruise Control on Driver Guidance - a Comparison between System Generations and Countries - Epple, Nico; Dari, Simone; Drees, Ludwig; Protschky, Valentin; Riener, Andreas (2019 IEEE Intelligent Vehicles Symposium (IV))

Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking - Tornede, Alexander; Wever, Marcel Dominik; Hüllermeier, Eyke (Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019)

A Reduction of Label Ranking to Multiclass Classification - Brinker, Klaus; Hüllermeier, Eyke (Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases)

Learning from Imprecise Data: Adjustments of Optimistic and Pessimistic Variants - Hüllermeier, Eyke; Couso, Ines; Diestercke, Sebastian (Proceedings SUM 2019, International Conference on Scalable Uncertainty Management)

Mining Rank Data - Henzgen, Sascha; Hüllermeier, Eyke (ACM Transactions on Knowledge Discovery from Data)

Asymptotic confidence sets for the jump curve in bivariate regression problems - Bengs, Viktor; Eulert, Matthias; Holzmann, Hajo (Journal of Multivariate Analysis)

Adaptive confidence sets for kink estimation - Bengs, Viktor; Holzmann, Hajo (Electronic Journal of Statistics)

From Automated to On-The-Fly Machine Learning - Mohr, Felix; Wever, Marcel Dominik; Tornede, Alexander; Hüllermeier, Eyke (INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft)

Automating Multi-Label Classification Extending ML-Plan - Wever, Marcel Dominik; Mohr, Felix; Tornede, Alexander; Hüllermeier, Eyke
Existing tools for automated machine learning, such as Auto-WEKA, TPOT, auto-sklearn, and more recently ML-Plan, have shown impressive results for the tasks of single-label classification and regression. Yet, there is only little work on other types of machine learning problems so far. In particular, there is almost no work on automating the engineering of machine learning solutions for multi-label classification (MLC). We show how the scope of ML-Plan, an AutoML-tool for multi-class classification, can be extended towards MLC using MEKA, which is a multi-label extension of the well-known Java library WEKA. The resulting approach recursively refines MEKA's multi-label classifiers, nesting other multi-label classifiers for meta algorithms and single-label classifiers provided by WEKA as base learners. In our evaluation, we find that the proposed approach yields strong results and performs significantly better than a set of baselines we compare with.

Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches - Rohlfing, Katharina; Leonardi, Giuseppe; Nomikou, Iris; Rączaszek-Leonardi, Joanna; Hüllermeier, Eyke (IEEE Transactions on Cognitive and Developmental Systems)

(WIP) Towards the Automated Composition of Machine Learning Services - Mohr, Felix; Wever, Marcel Dominik; Hüllermeier, Eyke; Faez, Amin (SCC)

Deep Architectures for Learning Context-dependent Ranking Functions - Pfannschmidt, Karlson; Gupta, Pritha; Hüllermeier, Eyke (arXiv:1803.05796)
Object ranking is an important problem in the realm of preference learning. On the basis of training data in the form of a set of rankings of objects, which are typically represented as feature vectors, the goal is to learn a ranking function that predicts a linear order of any new set of objects. Current approaches commonly focus on ranking by scoring, i.e., on learning an underlying latent utility function that seeks to capture the inherent utility of each object. These approaches, however, are not able to take possible effects of context-dependence into account, where context-dependence means that the utility or usefulness of an object may also depend on what other objects are available as alternatives. In this paper, we formalize the problem of context-dependent ranking and present two general approaches based on two natural representations of context-dependent ranking functions. Both approaches are instantiated by means of appropriate neural network architectures, which are evaluated on suitable benchmark task.

Programmatic Task Network Planning - Mohr, Felix; Lettmann, Theodor; Hüllermeier, Eyke; Wever, Marcel Dominik (Proceedings of the 1st ICAPS Workshop on Hierarchical Planning)

Stability of stochastic approximations with “controlled markov” noise and temporal difference learning - Ramaswamy, Arunselvan; Bhatnagar, Shalabh (IEEE Transactions on Automatic Control)

Deepcas: A deep reinforcement learning algorithm for control-aware scheduling - Demirel, Burak; Ramaswamy, Arunselvan; Quevedo, Daniel E; Karl, Holger (IEEE Control Systems Letters)

On-The-Fly Service Construction with Prototypes - Mohr, Felix; Wever, Marcel Dominik; Hüllermeier, Eyke (SCC)

On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis - Melnikov, Vitalik; Hüllermeier, Eyke (Machine Learning)
In machine learning, so-called nested dichotomies are utilized as a reduction technique, i.e., to decompose a multi-class classification problem into a set of binary problems, which are solved using a simple binary classifier as a base learner. The performance of the (multi-class) classifier thus produced strongly depends on the structure of the decomposition. In this paper, we conduct an empirical study, in which we compare existing heuristics for selecting a suitable structure in the form of a nested dichotomy. Moreover, we propose two additional heuristics as natural completions. One of them is the Best-of-K heuristic, which picks the (presumably) best among K randomly generated nested dichotomies. Surprisingly, and in spite of its simplicity, it turns out to outperform the state of the art.

ML-Plan: Automated Machine Learning via Hierarchical Planning - Mohr, Felix; Wever, Marcel Dominik; Hüllermeier, Eyke (Machine Learning)
Automated machine learning (AutoML) seeks to automatically select, compose, and parametrize machine learning algorithms, so as to achieve optimal performance on a given task (dataset). Although current approaches to AutoML have already produced impressive results, the field is still far from mature, and new techniques are still being developed. In this paper, we present ML-Plan, a new approach to AutoML based on hierarchical planning. To highlight the potential of this approach, we compare ML-Plan to the state-of-the-art frameworks Auto-WEKA, auto-sklearn, and TPOT. In an extensive series of experiments, we show that ML-Plan is highly competitive and often outperforms existing approaches.
Keywords: AutoML; Hierarchical Planning; HTN planning; ML-Plan

Reduction Stumps for Multi-Class Classification - Mohr, Felix; Wever, Marcel Dominik; Hüllermeier, Eyke (Proceedings of the Symposium on Intelligent Data Analysis)

ML-Plan for Unlimited-Length Machine Learning Pipelines - Wever, Marcel Dominik; Mohr, Felix; Hüllermeier, Eyke (ICML 2018 AutoML Workshop)
In automated machine learning (AutoML), the process of engineering machine learning applications with respect to a specific problem is (partially) automated. Various AutoML tools have already been introduced to provide out-of-the-box machine learning functionality. More specifically, by selecting machine learning algorithms and optimizing their hyperparameters, these tools produce a machine learning pipeline tailored to the problem at hand. Except for TPOT, all of these tools restrict the maximum number of processing steps of such a pipeline. However, as TPOT follows an evolutionary approach, it suffers from performance issues when dealing with larger datasets. In this paper, we present an alternative approach leveraging a hierarchical planning to configure machine learning pipelines that are unlimited in length. We evaluate our approach and find its performance to be competitive with other AutoML tools, including TPOT.
Keywords: automated machine learning; complex pipelines; hierarchical planning

Ensembles of Evolved Nested Dichotomies for Classification - Wever, Marcel Dominik; Mohr, Felix; Hüllermeier, Eyke (Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018)
In multinomial classification, reduction techniques are commonly used to decompose the original learning problem into several simpler problems. For example, by recursively bisecting the original set of classes, so-called nested dichotomies define a set of binary classification problems that are organized in the structure of a binary tree. In contrast to the existing one-shot heuristics for constructing nested dichotomies and motivated by recent work on algorithm configuration, we propose a genetic algorithm for optimizing the structure of such dichotomies. A key component of this approach is the proposed genetic representation that facilitates the application of standard genetic operators, while still supporting the exchange of partial solutions under recombination. We evaluate the approach in an extensive experimental study, showing that it yields classifiers with superior generalization performance.
Keywords: Classification; Hierarchical Decomposition; Indirect Encoding

Automated Multi-Label Classification based on ML-Plan - Wever, Marcel Dominik; Mohr, Felix; Hüllermeier, Eyke

Automated machine learning service composition - Mohr, Felix; Wever, Marcel Dominik; Hüllermeier, Eyke

Ranking of Classification Algorithms in AutoML - Graf, Helena

Learning about learning curves from dataset properties - Scheibl, Manuel

Preference-Based Reinforcement Learning Using Dyad Ranking - Schäfer, Dirk; Hüllermeier, Eyke (Discovery Science)

Research Directions for Principles of Data Management

Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators - Couso, Ines; Hüllermeier, Eyke (Frontiers in Computational Intelligence)

Dyad ranking using Plackett-Luce models based on joint feature representations - Schäfer, D.; Hüllermeier, Eyke (Machine Learning)

Learning to Rank Based on Analogical Reasoning - Ahmadi Fahandar, Mohsen; Hüllermeier, Eyke (Proc. 32 nd AAAI Conference on Artificial Intelligence (AAAI))

Ranking Distributions based on Noisy Sorting - El Mesaoudi-Paul, Adil; Hüllermeier, Eyke; Busa-Fekete, Robert (Proc. 35th Int. Conference on Machine Learning (ICML))

A Reinforcement Learning Strategy for the Swing-Up of the Double Pendulum on a Cart - Hesse, M.; Timmermann, J.; Hüllermeier, Eyke; Trächtler, Ansgar (Proc. 4th Int. Conference on System-Integrated Intelligence: Intelligent, Flexible and Connected Systems in Products and Production, Procedia Manufacturing 24)

Learning interpretable rules for multi-label classification - Mencia, E.Loza; Fürnkranz, J.; Hüllermeier, Eyke; Rapp, M. (Explainable and Interpretable Models in Computer Vision and Machine Learning)

Reliable Multi-class Classification based on Pairwise Epistemic and Aleatoric Uncertainty - Nguyen, Vu-Linh; Destercke, Sebastian; Masson, M.-H.; Hüllermeier, Eyke (Proc. 27th Int.Joint Conference on Artificial Intelligence (IJCAI))

Preference-Based Reinforcement Learning Using Dyad Ranking - Schäfer, Dirk; Hüllermeier, Eyke (Proc. 21st Int. Conference on Discovery Science (DS))

Dyad Ranking Using Plackett-Luce Models based on joint feature representations - Schäfer, Dirk; Hüllermeier, Eyke (Machine Learning)

Supporting the Cognitive Process in Annotation Tasks - Seemann, Nina; Geierhos, Michaela; Merten, Marie-Luis; Tophinke, Doris; Wever, Marcel Dominik; Hüllermeier, Eyke (Postersession Computerlinguistik der 40. Jahrestagung der Deutschen Gesellschaft für Sprachwissenschaft)

Analysis of gradient descent methods with nondiminishing bounded errors - Ramaswamy, Arunselvan; Bhatnagar, Shalabh (IEEE Transactions on Automatic Control)

A generalization of the Borkar-Meyn theorem for stochastic recursive inclusions - Ramaswamy, Arunselvan; Bhatnagar, Shalabh (Mathematics of Operations Research)

Optimizing the Structure of Nested Dichotomies: A Comparison of Two Heuristics - Melnikov, Vitalik; Hüllermeier, Eyke (Proceedings. 27. Workshop Computational Intelligence, Dortmund, 23. - 24. November 2017)

Certiﬁcation Matters for Service Markets - Jakobs, Marie-Christine; Krämer, Julia; van Straaten, Dirk; Lettmann, Theodor (The Ninth International Conferences on Advanced Service Computing (SERVICE COMPUTATION))
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.

Annotation Challenges for Reconstructing the Structural Elaboration of Middle Low German - Seemann, Nina; Merten, Marie-Luis; Geierhos, Michaela; Tophinke, Doris; Hüllermeier, Eyke (Proceedings of the Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature)
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.

Genetischer Algorithmus zur Erstellung von Ensembles von Nested Dichotomies - Schnitker, Nino Noel

jPL: A Java-based Software Framework for Preference Learning - Gupta, Pritha; Hetzer, Alexander; Tornede, Tanja; Gottschalk, Sebastian; Kornelsen, Andreas; Osterbrink, Sebastian; Pfannschmidt, Karlson; Hüllermeier, Eyke

Solving the Container Pre-Marshalling Problem using Reinforcement Learning and Structured Output Prediction - Hetzer, Alexander; Tornede, Tanja

Predicting Rankings of Software Verification Tools - Czech, Mike; Hüllermeier, Eyke; Jakobs, Marie-Christine; Wehrheim, Heike (Proceedings of the 3rd International Workshop on Software Analytics)
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).

Predicting Rankings of Software Verification Competitions - Czech, Mike; Hüllermeier, Eyke; Jakobs, Marie-Christine; Wehrheim, Heike
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).

Preference Learning - Fürnkranz, J.; Hüllermeier, Eyke (Encyclopedia of Machine Learning and Data Mining)

Preference Learning - Fürnkranz, J.; Hüllermeier, Eyke (Encyclopedia of Machine Learning and Data Mining)

Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization - Wever, Marcel Dominik; Mohr, Felix; Hüllermeier, Eyke (27th Workshop Computational Intelligence)
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.

Optimizing the structure of nested dichotomies. A comparison of two heuristics - Melnikov, Vitaly; Hüllermeier, Eyke (in Proceedings 27th Workshop Computational Intelligence, Dortmund Germany)

Predicting rankings of software verification tools - Czech, M.; Hüllermeier, Eyke; Jacobs, M.C.; Wehrheim, Heike (in Proceedings ESEC/FSE Workshops 2017 - 3rd ACM SIGSOFT, International Workshop on Software Analytics (SWAN 2017), Paderborn Germany)

Maximum likelihood estimation and coarse data - Couso, Ines; Dubois, D.; Hüllermeier, Eyke (in Proceedings SUM 2017, 11th International Conference on Scalable Uncertainty Management, Granada, Spain)

Estimating relative depth in single images via rankboost - Ewerth, Ralph; Springstein, M.; Müller, E.; Balz, A.; Gehlhaar, J.; Naziyok, T.; Dembczynski, K.; Hüllermeier, Eyke (Proc. IEEE Int. Conf. on Multimedia and Expo (ICME 2017))

Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent Coarsening - Ahmadi Fahandar, Mohsen; Hüllermeier, Eyke; Couso, Ines (Proc. 34th Int. Conf. on Machine Learning (ICML 2017))

Planning with Independent Task Networks - Mohr, Felix; Lettmann, Theodor; Hüllermeier, Eyke (Proc. 40th Annual German Conference on Advances in Artificial Intelligence (KI 2017))

Predicting rankings of software verification tools - Czech, M.; Hüllermeier, Eyke; Jakobs, M.-C.; Wehrheim, Heike (Proc. 3rd ACM SIGSOFT Int. I Workshop on Software Analytics (SWAN@ESEC/SIGSOFT FSE 2017)

Maximum Likelihood Estimation and Coarse Data - Couso, Ines; Dubois, D.; Hüllermeier, Eyke (Proc. 11th Int. Conf. on Scalable Uncertainty Management (SUM 2017))

Learning to Rank based on Analogical Reasoning - Ahmadi Fahandar, Mohsen; Hüllermeier, Eyke (Proc. AAAI 2017, 32nd AAAI Conference on Artificial Intelligence)

(Hrsg.) Proceedings 27. Workshop Computational Intelligence, KIT Scientific Publishing, Karlsruhe, Germany 2017 - Hoffmann, F.; Hüllermeier, Eyke; Mikut, R.

Optimizing the Structure of Nested Dichotomies: A Comparison of Two Heuristics - Melnikov, Vitaly; Hüllermeier, Eyke (Proceedings 27. Workshop Computational Intelligence, Dortmund, Germany 2017)

Learning TSK Fuzzy Rules from Data Streams - Shaker, Ammar; Heldt, W.; Hüllermeier, Eyke (Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases, Skopje, Macedonia)

Lexicographic preferences for predictive modeling of human decision making. A new machine learning method with an application in accounting - Bräuning, M.; Hüllermeier, Eyke; Keller, T.; Glaum, M. (European Journal of Operational Research)

Imprecise Matching of Requirements Specifications for Software Services Using Fuzzy Logic - Platenius, M.-C.; Shaker, Ammar; Becker, M.; Hüllermeier, Eyke; Schäfer, W. (IEEE Transactions on Software Engineering)

From Knowledge-based to Data-driven Modeling of Fuzzy Rule-based Systems: A Critical Reflection - Hüllermeier, Eyke (The Computing Research Repository (CoRR))

Stochastic recursive inclusion in two timescales with an application to the lagrangian dual problem - Ramaswamy, Arunselvan; Bhatnagar, Shalabh (Stochastics)

Pairwise versus Pointwise Ranking: A Case Study - Melnikov, Vitalik; Hüllermeier, Eyke; Kaimann, Daniel; Frick, Bernd; Gupta, Pritha (Schedae Informaticae)

Imprecise Matching of Requirements Specifications for Software Services using Fuzzy Logic - Platenius, Marie Christin; Shaker, Ammar; Becker, Matthias; Hüllermeier, Eyke; Schäfer, Wilhelm (IEEE Transactions on Software Engineering (TSE), presented at ICSE 2017)
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.

Learning to Aggregate Using Uninorms - Melnikov, Vitaly; Hüllermeier, Eyke (Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2016))
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.

Preference Learning - Fürnkranz, J.; Hüllermeier, Eyke (Encyclopedia of Machine Learning and Data Mining)

On the identifiability of models in multi-criteria preference learning - Labreuche, C.; Hüllermeier, Eyke; Vojtas, P.; Fallah Tehrani, A. (in Proceedings DA2PL 2016 EURO Mini Conference From Multiple Criteria Decision Aid to Preference Learning, Paderborn Germany)

Preference -based reinforcement learning using dyad ranking - Schäfer, D.; Hüllermeier, Eyke (in Proceedings DA2PL`2016 Euro Mini Conference From Multiple Criteria Decision Aid to Preference Learning, Paderborn, Germany)

Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators - Couso, Ines; Ahmadi Fahandar, Mohsen; Hüllermeier, Eyke (in Proceedings DA2PL 2016 EURO Mini Conference From Multiple Criteria Decision Aid to Preference Learning, Paderborn Germany)

Support vector classification on noisy data using fuzzy superset losses - Lu, S.; Hüllermeier, Eyke (in Proceedings 26th Workshop Computational Intelligence, Dortmund Germany)

Plackett-Luce networks for dyad ranking - Schäfer, D.; Hüllermeier, Eyke (in Workshop LWDA "Lernen, Wissen, Daten, Analysen" Potsdam, Germany)

Evaluating tests in medical diagnosis-Combining machine learning with game-theoretical concepts - Pfannschmidt, Karlson; Hüllermeier, Eyke; Held, S.; Neiger, R. (In Proceedings IPMU 16th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Part 1, Eindhoven, The Netherlands)

CavSimBase: A database for large scale comparison of protein binding sites - Leinweber, M.; Fober, T.; Strickert, M.; Baumgärtner, L.; Klebe, G.; Freisleben, B.; Hüllermeier, Eyke (IEEE Transactions on Knowledge and Data Engineering)

Towards Automated Service Composition Under Quality Constraints - Mohr, Felix

Preference Learning - Fürnkranz, J.; Hüllermeier, Eyke (Encyclopedia of Machine Learning and Data Mining)

Proceedings 26. Workshop Computational Intelligence KIT Scientific Publishing, Karlsruhe, Germany

Extreme F-measure maximization using sparse probability estimates - Jasinska, K.; Dembczynski, K.; Busa-Fekete, Robert; Klerx, Timo; Hüllermeier, Eyke (Proceedings ICML-2016, 33th International Conference on Machine Learning, New York, USA)

Learning to aggregate using uninorms, in Proceedings ECML/PKDD-2016 - Melnikov, Vitaly; Hüllermeier, Eyke (European Conference on Machine Learning and Knowledge Discovery in Databases, Part II, Riva del Garda, Italy)

Consistency of probalistic classifier trees - Dembczynski, K.; Kotlowski, W.; Waegeman, W.; Busa-Fekete, Robert; Hüllermeier, Eyke (In Proceedings ECML/PKDD European Conference on Maschine Learning and Knowledge Discovery in Databases, Part II, Riva del Garda, Italy)

Predicting the electricity consumption of buildings: An improved CBR approach - Shabani, Aulon; Paul, Adil; Platon, R.; Hüllermeier, Eyke (In Proceedings ICCBR, 24th International Conference on Case-Based Reasoning, Atlanta, GA, USA)

Evaluating tests in medical diagnosis-Combining machine learning with game-theoretical concepts - Pfannschmidt, Karlson; Hüllermeier, Eyke; Held, S.; Neiger, R. (In Proceedings IPMU 16th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Part 1, Eindhoven, The Netherlands)

On the Identifiability of models in multi-criteria preference learning - Labreuche, C.; Hüllermeier, Eyke; Vojtas, P.; Fallah Tehrani, A. (Proceedings DA2PL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning)

Preference-Based Reinforcement Learning Using Dyad Ranking - Schäfer, Dirk; Hüllermeier, Eyke (Proceedings DA2PL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning)

Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators - Couso, Ines; Ahmadi Fahandar, Mohsen; Hüllermeier, Eyke (Proceedings DA2PL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning)

Support vector classification on noisy data using fuzzy supersets losses - Lu, S.; Hüllermeier, Eyke (Proceedings 26. Workshop Computational Intelligence, KIT Scientific Publishing)

Plackett-Luce networks for dyad ranking - Schäfer, Dirk; Hüllermeier, Eyke (In Workshop LWDA "Lernen, Wissen, Daten, Analysen")