On the Bayes-Optimality of F-Measure Maximizers - Waegeman, W.; Dembczynski, K.; Jachnik, A.; Cheng, W.; Hüllermeier, Eyke (in Journal of Machine Learning Research)

Does machine learning need fuzzy logic? - Hüllermeier, Eyke (Fuzzy Sets and Systems)

Recovery analysis for adaptive learning from non-stationary data streams: Experimental design and case study - Shaker, Ammar; Hüllermeier, Eyke (Neurocomputing)

From Knowledge-based to Data-driven fuzzy modeling-Development, criticism and alternative directions - Hüllermeier, Eyke (Informatik Spektrum)

Overlap Indices: Construction of and Application of Interpolative Fuzzy Systems - Garcia-Jimenez, S.; Bustince, U.; Hüllermeier, Eyke; Mesiar, R.; Pal, N.R.; Pradera, A. (IEEE Transactions on Fuzzy Systems)

Fast Fuzzy Pattern Tree Learning of Classification - Senge, Robin; Hüllermeier, Eyke (IEEE Transactions on Fuzzy Systems)

Rainbow connection number of graph power and graph products - Basavaraju, Manu; Chandran, L Sunil; Rajendraprasad, Deepak; Ramaswamy, Arunselvan (Graphs and Combinatorics)

Rainbow connection number and radius - Basavaraju, Manu; Chandran, L Sunil; Rajendraprasad, Deepak; Ramaswamy, Arunselvan (Graphs and Combinatorics)

Template-based Generation of Semantic Services - Mohr, Felix; Walther, Sven (Proceedings of the 14th International Conference on Software Reuse (ICSR))
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.

Combining Automatic Service Composition with Adaptive Service Recommendation for Dynamic Markets of Services - Jungmann, Alexander; Mohr, Felix; Kleinjohann, Bernd (Proceedings of the 10th World Congress on Services (SERVICES))
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.

Applying Reinforcement Learning for Resolving Ambiguity in Service Composition - Jungmann, Alexander; Mohr, Felix; Kleinjohann, Bernd (Proceedings of the 7th International Conference on Service Oriented Computing and Applications (SOCA))
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.

Estimating Functional Reusability of Services - Mohr, Felix (Proceedings of the 12th International Conference on Service Oriented Computing (ICSOC))
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.

Preference-based learning of ideal solutions in TOPSIS-like decision models - Agarwal, M.; Fallah Tehrani, A.; Hüllermeier, Eyke (Journal of Multi-Criteria Decision Analysis)

Extended graph-based models for enhanced similarity search in Cabase - Krotzky, T.; Fober, T.; Hüllermeier, Eyke; Klebe, G. (IEEE/ACM Transactions of Computational Biology and Bioinformatics)

Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization - Hüllermeier, Eyke (International Journal of Approximate Reasoning)

Visualization of evolving fuzzy-rule-based systems - Henzgen, Sascha; Strickert, M.; Hüllermeier, Eyke (Evolving Systems)

Preference-based reinforcement learning: evolutionary direct policy search using a preference-based racing algorithm. - Busa-Fekete, Robert; Szörenyi, B.; Weng, P.; Cheng, W.; Hüllermeier, Eyke (Machine Learning)

Open challenges for data stream mining research - Krempl, G.; Zliobaite, I.; Brzezinski, D.; Hüllermeier, Eyke; Last, M.; Lemaire, V.; Noack, T.; Shaker, A.; Sievi, S.; Spiliopoulou, M.; Stefanowski, J. (SIGKDD Explorations)

Correlation-based embedding of pairwise score data - Strickert, M.; Bunte, K.; Schleif, F.M.; Hüllermeier, Eyke (Neurocomputing)

Survival analysis on data streams: Analyzing temporal events in dynamically changing environments - Shaker, Ammar; Hüllermeier, Eyke (International Journal of Applied Mathematics and Computer Science)

Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty - Senge, Robin; Bösner, S.; Dembczynski, K.; Haasenritter, J.; Hirsch, O.; Donner-Banzhoff, N.; Hüllermeier, Eyke (Information Sciences)

The comprehensive diagnostic study is suggested as a design to model the diagnostic process - Donner-Banzhoff, N.; Haasenritter, J.; Hüllermeier, Eyke; Viniol, A.; Bösner, S.; Becker, A. (Journal of Clinical Epidemiology)

PAC Rank Elicitation through Adaptive Sampling of Stochastic Pairwise Preferences - Busa-Fekete, Robert; Szörényi, B.; Hüllermeier, Eyke (Proceedings AAAI 2014, Quebec, Canada)

A Survey of Preference-Based Online Learning with Bandit Algorithms - Busa-Fekete, Robert; Hüllermeier, Eyke (Proceedings Int. Conf. on Algorithmic Learning Theory (ALT), Bled, Slovenia)

Mining Rank Data - Henzgen, Sascha; Hüllermeier, Eyke (Proceedings Discovery Science, Bled,Slovenia)

The Choquet kernel for monotone data - Fallah Tehrani, A.; Strickert, M.; Hüllermeier, Eyke (Proceedings ESANN , Bruges, Belgium)

Learning Solution Similarity in Preference-Based CBR - Abdel-Aziz, A.; Strickert, M.; Hüllermeier, Eyke (Proceedings Int. Conf. Case-Based Reasoning (ICCBR), Cork, Ireland)

Dyad Ranking Using A Bilinear Plackett-Luce Model - Schäfer, Dirk; Hüllermeier, Eyke (Proceedings Lernen-Wissensentdeckung-Adaptivität (LWA), Aachen, Germany)

Machine Learning and Knowledge Discovery in Databases-European Conf. ECML/PKDD, Nancy, France - Calders, T.; Esposito, F.; Hüllermeier, Eyke; Meo, R. (Proceedings, Parts I-III. Lecture Notes in Computer Science)

Preference Learning (Dagstuhl Seminar 14101) Dagstuhl Reports - Fürnkranz, J.; Hüllermeier, Eyke; Rudin, Cynthia; Slowinski, Roman; Sanner, Scott

Survival analysis on data streams: Analyzing temporal events in dynamically changing environments - Shaker, Ammar; Hüllermeier, Eyke (Applied Mathematics and Computer Science)

Ausgewählte Beiträge des GMA-Fachausschusses 5.14 - Hoffmann, F.; Hüllermeier, Eyke; Kroll, A. (Computational Intelligence Automatisierungstechnik)

Guest editors`introduction:special issue of the ECML/PKDD 2014 journal track - Calders, T.; Esposito, F.; Hüllermeier, Eyke; Meo, R. (Data Min. Knowledge Discovery)

Visualization of evolving fuzzy rule-based systems - Henzgen, Sascha; Strickert, M.; Hüllermeier, Eyke (Evolving Systems)

Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization - Hüllermeier, Eyke (Int. J. Approx. Reasoning)

Rejoinder on "Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization - Hüllermeier, Eyke (Int. J. Approx. Reasoning)

Correlation-based embedding of pairwise score data - Strickert, M.; Bunte, K.; Schleif, F.- M.; Hüllermeier, Eyke (Neurocomputing)

Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty - Senge, Robin; Bösner, S.; Dembczynski, K.; Haasenritter, J.; Hirsch, O.; Donner-Banzhoff, N.; Hüllermeier, Eyke (Information Sciences)

Protein Sub-Cellular Localization Prediction for Special compartments via Optimized Time Series Distances - Mernberger, M.; Moog, M.; Stork, S.; Zauner, S.; Maier, U.G.; Hüllermeier, Eyke (J. Bioinformatics and Computational Biology)

Guest editors`introduction:special issue of the ECML/PKDD 2014 journal track - Calders, T.; Esposito, F.; Hüllermeier, Eyke; Meo, R. (Machine Learning)

Preference-Based Reinforcement Learning: evolutionary direct policy search using a preference-based racing algorithm - Busa-Fekete, Robert; Szörényi, B.; Weng, P.; Cheng, W.; Hüllermeier, Eyke (Machine Learning)

Dependent binary relevance models for multi-label classification - Montanés, E.; Senge, Robin; Barranquero, J.; Quevedo, J.R.; Del Coz, J.J.; Hüllermeier, Eyke (Pattern Recognition)

Open challenges for data stream mining research - Krempl, G.; Zliobaite, I.; Brzezinski, D.; Hüllermeier, Eyke; Last, M.; Lemaire, V.; Noack, T.; Shaker, Ammar; Sievi, S.; Spiliopoulou, M.; Stefanowski, J. (SIGKDD Explorations)

Extended Graph-Based Models for Enhanced Similarity Search in Cavbase - Krotzky, T.; Fober, T.; Hüllermeier, Eyke; Klebe, G. (IEEE/ACM Trans. Comput. Biology Bioinform.)

Identification of Functionally Releated Enzymes by Learning to Rank Methods - Stock, M.; Fober, T.; Hüllermeier, Eyke; Glinca, S,; Klebe, G.; Pahikkala, T.; Airola, A.; De Baets, B.; Wageman, W. (IEEE/ACM Trans. Comput. Biology Bioinform.)

Semi-Automated Software Composition Through Generated Components - Mohr, Felix; Kleine Büning, Hans (Proceedings of the 15th International Conference on Information Integration and Web-based Applications & Services (iiWAS))
Software composition has been studied as a subject of state based planning for decades. Existing composition approaches that are efficient enough to be used in practice are limited to sequential arrangements of software components. This restriction dramatically reduces the number of composition problems that can be solved. However, there are many composition problems that could be solved by existing approaches if they had a possibility to combine components in very simple non-sequential ways. To this end, we present an approach that arranges not only basic components but also composite components. Composite components enhance the structure of the composition by conditional control flows. Through algorithms that are written by experts, composite components are automatically generated before the composition process starts. Therefore, our approach is not a substitute for existing composition algorithms but complements them with a preprocessing step. We verified the validity of our approach through implementation of the presented algorithms.

Reducing Nondeterminism in Automated Service Composition - Mohr, Felix; Lettmann, Theodor; Kleine Büning, Hans (Proceedings of the 6th International Conference on Service Oriented Computing and Applications (SOCA))
Automated service composition has been studied as a subject of state based planning for a decade. A great deal of service composition tasks can only be solved if concrete output values of the services are considered in the composition process. However, the fact that those values are not known before runtime leads to nondeterministic planning problems, which have proven to be notoriously difficult in practical automated service composition applications. Even though this problem is frequently recognized, it has still received remarkably few attention and remains unsolved.This paper shows how nondeterminism in automated service composition can be reduced. We introduce context rules as a means to derive semantic knowledge from output values of services. These rules enable us to replace nondeterministic composition operations by less nondeterministic or even completely deterministic ones. We show the validity of our solutions not only theoretically but also have evaluated them practically through implementation.

Labelwise versus pairwise decomposition in label ranking - Cheng, W.; Henzgen, S.; Hüllermeier, Eyke (In Proceedings Workshop LWA-2009, Lernen-Wissensentdeckung-Adaptivität, Bamberg, Germany)

Rectifying classifier chains for multi-label classification, Bamberg, Germany - Senge, Robin; del Coz, J.; Hüllermeier, Eyke (In Proceedings Workshop LWA-2009, Lernen-Wissensentdeckung-Adaptivität, Bamberg, Germany)

Preference-based evolutionary optimization using generalized racing algorithms - Busa-Fekete, Robert; Fober, T.; Hüllermeier, Eyke (in Proceedings 23th Workshop Computational Intelligence, Dortmund Germany)