@inproceedings{2109, abstract = {{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.}}, author = {{Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018}}, keywords = {{Classification, Hierarchical Decomposition, Indirect Encoding}}, location = {{Kyoto, Japan}}, publisher = {{ACM}}, title = {{{Ensembles of Evolved Nested Dichotomies for Classification}}}, doi = {{10.1145/3205455.3205562}}, year = {{2018}}, } @unpublished{17713, author = {{Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}}, publisher = {{Arxiv}}, title = {{{Automated Multi-Label Classification based on ML-Plan}}}, year = {{2018}}, } @unpublished{17714, author = {{Mohr, Felix and Wever, Marcel Dominik and Hüllermeier, Eyke}}, title = {{{Automated machine learning service composition}}}, year = {{2018}}, } @misc{5693, author = {{Graf, Helena}}, publisher = {{Universität Paderborn}}, title = {{{Ranking of Classification Algorithms in AutoML}}}, year = {{2018}}, } @misc{5936, author = {{Scheibl, Manuel}}, publisher = {{Universität Paderborn}}, title = {{{Learning about learning curves from dataset properties}}}, year = {{2018}}, } @inbook{6423, author = {{Schäfer, Dirk and Hüllermeier, Eyke}}, booktitle = {{Discovery Science}}, isbn = {{9783030017705}}, issn = {{0302-9743}}, pages = {{161--175}}, publisher = {{Springer International Publishing}}, title = {{{Preference-Based Reinforcement Learning Using Dyad Ranking}}}, doi = {{10.1007/978-3-030-01771-2_11}}, year = {{2018}}, } @proceedings{10591, editor = {{Abiteboul, S. and Arenas, M. and Barceló, P. and Bienvenu, M. and Calvanese, D. and David, C. and Hull, R. and Hüllermeier, Eyke and Kimelfeld, B. and Libkin, L. and Martens, W. and Milo, T. and Murlak, F. and Neven, F. and Ortiz, M. and Schwentick, T. and Stoyanovich, J. and Su, J. and Suciu, D. and Vianu, V. and Yi, K.}}, number = {{1}}, pages = {{1--29}}, title = {{{Research Directions for Principles of Data Management}}}, volume = {{7}}, year = {{2018}}, } @inbook{10783, author = {{Couso, Ines and Hüllermeier, Eyke}}, booktitle = {{Frontiers in Computational Intelligence}}, editor = {{Mostaghim, Sanaz and Nürnberger, Andreas and Borgelt, Christian}}, pages = {{31--46}}, publisher = {{Springer}}, title = {{{Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators}}}, year = {{2018}}, } @article{16038, author = {{Schäfer, D. and Hüllermeier, Eyke}}, journal = {{Machine Learning}}, number = {{5}}, pages = {{903--941}}, title = {{{Dyad ranking using Plackett-Luce models based on joint feature representations}}}, volume = {{107}}, year = {{2018}}, } @inproceedings{10145, author = {{Ahmadi Fahandar, Mohsen and Hüllermeier, Eyke}}, booktitle = {{Proc. 32 nd AAAI Conference on Artificial Intelligence (AAAI)}}, pages = {{2951--2958}}, title = {{{Learning to Rank Based on Analogical Reasoning}}}, year = {{2018}}, } @inproceedings{10148, author = {{El Mesaoudi-Paul, Adil and Hüllermeier, Eyke and Busa-Fekete, Robert}}, booktitle = {{Proc. 35th Int. Conference on Machine Learning (ICML)}}, pages = {{3469--3477}}, publisher = {{Verlagsschriftenreihe des Heinz Nixdorf Instituts, Paderborn}}, title = {{{Ranking Distributions based on Noisy Sorting}}}, year = {{2018}}, } @inproceedings{10149, author = {{Hesse, M. and Timmermann, J. and Hüllermeier, Eyke and Trächtler, Ansgar}}, booktitle = {{Proc. 4th Int. Conference on System-Integrated Intelligence: Intelligent, Flexible and Connected Systems in Products and Production, Procedia Manufacturing 24}}, pages = {{15--20}}, title = {{{A Reinforcement Learning Strategy for the Swing-Up of the Double Pendulum on a Cart}}}, year = {{2018}}, } @inbook{10152, author = {{Mencia, E.Loza and Fürnkranz, J. and Hüllermeier, Eyke and Rapp, M.}}, booktitle = {{Explainable and Interpretable Models in Computer Vision and Machine Learning}}, editor = {{Jair Escalante, H. and Escalera, S. and Guyon, I. and Baro, X. and Güclüütürk, Y. and Güclü, U. and van Gerven, M.A.J.}}, pages = {{81--113}}, publisher = {{Springer}}, title = {{{Learning interpretable rules for multi-label classification}}}, year = {{2018}}, } @inproceedings{10181, author = {{Nguyen, Vu-Linh and Destercke, Sebastian and Masson, M.-H. and Hüllermeier, Eyke}}, booktitle = {{Proc. 27th Int.Joint Conference on Artificial Intelligence (IJCAI)}}, pages = {{5089--5095}}, title = {{{Reliable Multi-class Classification based on Pairwise Epistemic and Aleatoric Uncertainty}}}, year = {{2018}}, } @inproceedings{10184, author = {{Schäfer, Dirk and Hüllermeier, Eyke}}, booktitle = {{Proc. 21st Int. Conference on Discovery Science (DS)}}, pages = {{161--175}}, title = {{{Preference-Based Reinforcement Learning Using Dyad Ranking}}}, year = {{2018}}, } @article{10276, author = {{Schäfer, Dirk and Hüllermeier, Eyke}}, journal = {{Machine Learning}}, number = {{5}}, pages = {{903--941}}, title = {{{Dyad Ranking Using Plackett-Luce Models based on joint feature representations}}}, volume = {{107}}, year = {{2018}}, } @inproceedings{1379, author = {{Seemann, Nina and Geierhos, Michaela and Merten, Marie-Luis and Tophinke, Doris and Wever, Marcel Dominik and Hüllermeier, Eyke}}, booktitle = {{Postersession Computerlinguistik der 40. Jahrestagung der Deutschen Gesellschaft für Sprachwissenschaft}}, editor = {{Eckart, Kerstin and Schlechtweg, Dominik }}, location = {{Stuttgart, Germany}}, title = {{{Supporting the Cognitive Process in Annotation Tasks}}}, year = {{2018}}, } @article{24152, author = {{Ramaswamy, Arunselvan and Bhatnagar, Shalabh}}, journal = {{IEEE Transactions on Automatic Control}}, number = {{5}}, pages = {{1465--1471}}, publisher = {{IEEE}}, title = {{{Analysis of gradient descent methods with nondiminishing bounded errors}}}, volume = {{63}}, year = {{2017}}, } @article{24153, author = {{Ramaswamy, Arunselvan and Bhatnagar, Shalabh}}, journal = {{Mathematics of Operations Research}}, number = {{3}}, pages = {{648--661}}, publisher = {{INFORMS}}, title = {{{A generalization of the Borkar-Meyn theorem for stochastic recursive inclusions}}}, volume = {{42}}, year = {{2017}}, } @inproceedings{3325, author = {{Melnikov, Vitalik and Hüllermeier, Eyke}}, booktitle = {{Proceedings. 27. Workshop Computational Intelligence, Dortmund, 23. - 24. November 2017}}, publisher = {{KIT Scientific Publishing}}, title = {{{Optimizing the Structure of Nested Dichotomies: A Comparison of Two Heuristics}}}, doi = {{10.5445/KSP/1000074341}}, year = {{2017}}, }