@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}}, }