@proceedings{10221, editor = {{Hoffmann, F. and Hüllermeier, Eyke and Mikut, R.}}, title = {{{ Proceedings 26. Workshop Computational Intelligence KIT Scientific Publishing, Karlsruhe, Germany}}}, year = {{2016}}, } @inproceedings{10222, author = {{Jasinska, K. and Dembczynski, K. and Busa-Fekete, Robert and Klerx, Timo and Hüllermeier, Eyke}}, booktitle = {{Proceedings ICML-2016, 33th International Conference on Machine Learning, New York, USA}}, editor = {{Balcan, M.F. and Weinberger, K.Q.}}, title = {{{Extreme F-measure maximization using sparse probability estimates }}}, year = {{2016}}, } @inproceedings{10223, author = {{Melnikov, Vitaly and Hüllermeier, Eyke}}, booktitle = {{European Conference on Machine Learning and Knowledge Discovery in Databases, Part II, Riva del Garda, Italy}}, pages = {{756--771}}, title = {{{Learning to aggregate using uninorms, in Proceedings ECML/PKDD-2016}}}, year = {{2016}}, } @inproceedings{10224, author = {{Dembczynski, K. and Kotlowski, W. and Waegeman, W. and Busa-Fekete, Robert and Hüllermeier, Eyke}}, booktitle = {{In Proceedings ECML/PKDD European Conference on Maschine Learning and Knowledge Discovery in Databases, Part II, Riva del Garda, Italy}}, pages = {{511--526}}, title = {{{Consistency of probalistic classifier trees}}}, year = {{2016}}, } @inproceedings{10225, author = {{Shabani, Aulon and Paul, Adil and Platon, R. and Hüllermeier, Eyke}}, booktitle = {{In Proceedings ICCBR, 24th International Conference on Case-Based Reasoning, Atlanta, GA, USA}}, pages = {{356--369}}, title = {{{Predicting the electricity consumption of buildings: An improved CBR approach}}}, year = {{2016}}, } @inproceedings{10226, author = {{Pfannschmidt, Karlson and Hüllermeier, Eyke and Held, S. and Neiger, R.}}, booktitle = {{In Proceedings IPMU 16th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Part 1, Eindhoven, The Netherlands}}, pages = {{450--461}}, publisher = {{Springer}}, title = {{{Evaluating tests in medical diagnosis-Combining machine learning with game-theoretical concepts}}}, year = {{2016}}, } @inproceedings{10227, author = {{Labreuche, C. and Hüllermeier, Eyke and Vojtas, P. and Fallah Tehrani, A.}}, booktitle = {{Proceedings DA2PL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning}}, editor = {{Busa-Fekete, Robert and Hüllermeier, Eyke and Mousseau, V. and Pfannschmidt, Karlson}}, title = {{{On the Identifiability of models in multi-criteria preference learning }}}, year = {{2016}}, } @inproceedings{10228, author = {{Schäfer, Dirk and Hüllermeier, Eyke}}, booktitle = {{Proceedings DA2PL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning}}, editor = {{Busa-Fekete, Robert and Hüllermeier, Eyke and Mousseau, V. and Pfannschmidt, Karlson}}, title = {{{Preference-Based Reinforcement Learning Using Dyad Ranking}}}, year = {{2016}}, } @inproceedings{10229, author = {{Couso, Ines and Ahmadi Fahandar, Mohsen and Hüllermeier, Eyke}}, booktitle = {{Proceedings DA2PL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning}}, editor = {{Busa-Fekete, Robert and Hüllermeier, Eyke and Mousseau, V. and Pfannschmidt, Karlson}}, title = {{{Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators}}}, year = {{2016}}, } @inproceedings{10230, author = {{Lu, S. and Hüllermeier, Eyke}}, booktitle = {{Proceedings 26. Workshop Computational Intelligence, KIT Scientific Publishing}}, editor = {{Hoffmann, F. and Hüllermeier, Eyke and Mikut, R.}}, pages = {{1--8}}, title = {{{Support vector classification on noisy data using fuzzy supersets losses}}}, year = {{2016}}, } @inproceedings{10231, author = {{Schäfer, Dirk and Hüllermeier, Eyke}}, booktitle = {{In Workshop LWDA "Lernen, Wissen, Daten, Analysen"}}, title = {{{Plackett-Luce networks for dyad ranking}}}, year = {{2016}}, } @proceedings{10263, editor = {{Kaminka, G.A. and Fox, M. and Bouquet, P. and Hüllermeier, Eyke and Dignum, V. and Dignum, F. and van Harmelen, F.}}, publisher = {{IOS Press}}, title = {{{ECAI 2016, 22nd European Conference on Artificial Intelligence, including PAIS 2016, Prestigious Applications of Artificial Intelligence}}}, volume = {{285}}, year = {{2016}}, } @article{10264, author = {{Leinweber, M. and Fober, T. and Strickert, M. and Baumgärtner, L. and Klebe, G. and Freisleben, B. and Hüllermeier, Eyke}}, journal = {{IEEE Transactions on Knowledge and Data Engineering}}, number = {{6}}, pages = {{1423--1434}}, title = {{{CavSimBase: A database for large scale comparison of protein binding sites}}}, volume = {{28}}, year = {{2016}}, } @article{10266, author = {{Riemenschneider, M. and Senge, Robin and Neumann, U. and Hüllermeier, Eyke and Heider, D.}}, journal = {{BioData Mining}}, number = {{10}}, title = {{{Exploiting HIV-1 protease and reverse transcriptase cross-resistance information for improved drug resistance prediction by means of multi-label classification}}}, volume = {{9}}, year = {{2016}}, } @article{1373, author = {{Herlich, Matthias and Bredenbals, Nico and Karl, Holger}}, issn = {{2210-5379}}, journal = {{Sustainable Computing: Informatics and Systems}}, pages = {{48--55}}, publisher = {{Elsevier BV}}, title = {{{Delayed (de-)activation in servers with a sleep mode}}}, doi = {{10.1016/j.suscom.2016.04.002}}, volume = {{10}}, year = {{2016}}, } @inproceedings{137, abstract = {{Wikidata is the new, large-scale knowledge base of the Wikimedia Foundation. Its knowledge is increasingly used within Wikipedia itself and various other kinds of information systems, imposing high demands on its integrity.Wikidata can be edited by anyone and, unfortunately, it frequently gets vandalized, exposing all information systems using it to the risk of spreading vandalized and falsified information. In this paper, we present a new machine learning-based approach to detect vandalism in Wikidata.We propose a set of 47 features that exploit both content and context information, and we report on 4 classifiers of increasing effectiveness tailored to this learning task. Our approach is evaluated on the recently published Wikidata Vandalism Corpus WDVC-2015 and it achieves an area under curve value of the receiver operating characteristic, ROC-AUC, of 0.991. It significantly outperforms the state of the art represented by the rule-based Wikidata Abuse Filter (0.865 ROC-AUC) and a prototypical vandalism detector recently introduced by Wikimedia within the Objective Revision Evaluation Service (0.859 ROC-AUC).}}, author = {{Heindorf, Stefan and Potthast, Matthias and Stein, Benno and Engels, Gregor}}, booktitle = {{Proceedings of the 25th International Conference on Information and Knowledge Management (CIKM 2016)}}, pages = {{327----336}}, title = {{{Vandalism Detection in Wikidata}}}, doi = {{10.1145/2983323.2983740}}, year = {{2016}}, } @inbook{2978, author = {{Blömer, Johannes and Bujna, Kathrin}}, booktitle = {{Advances in Knowledge Discovery and Data Mining}}, isbn = {{9783319317496}}, issn = {{0302-9743}}, pages = {{296--308}}, publisher = {{Springer International Publishing}}, title = {{{Adaptive Seeding for Gaussian Mixture Models}}}, doi = {{10.1007/978-3-319-31750-2_24}}, year = {{2016}}, } @article{48306, abstract = {{The goal of argumentation mining, an evolving research field in computational linguistics, is to design methods capable of analyzing people's argumentation. In this article, we go beyond the state of the art in several ways. (i) We deal with actual Web data and take up the challenges given by the variety of registers, multiple domains, and unrestricted noisy user-generated Web discourse. (ii) We bridge the gap between normative argumentation theories and argumentation phenomena encountered in actual data by adapting an argumentation model tested in an extensive annotation study. (iii) We create a new gold standard corpus (90k tokens in 340 documents) and experiment with several machine learning methods to identify argument components. We offer the data, source codes, and annotation guidelines to the community under free licenses. Our findings show that argumentation mining in user-generated Web discourse is a feasible but challenging task.}}, author = {{Habernal, Ivan and Gurevych, Iryna}}, issn = {{0891-2017}}, journal = {{Computational Linguistics}}, keywords = {{Artificial Intelligence, Computer Science Applications, Linguistics and Language, Language and Linguistics}}, number = {{1}}, pages = {{125--179}}, publisher = {{MIT Press}}, title = {{{Argumentation Mining in User-Generated Web Discourse}}}, doi = {{10.1162/coli_a_00276}}, volume = {{43}}, year = {{2016}}, } @inproceedings{48308, author = {{Habernal, Ivan and Sukhareva, Maria and Raiber, Fiana and Shtok, Anna and Kurland, Oren and Ronen, Hadar and Bar-Ilan, Judit and Gurevych, Iryna}}, booktitle = {{Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval}}, publisher = {{ACM}}, title = {{{New Collection Announcement}}}, doi = {{10.1145/2911451.2914682}}, year = {{2016}}, } @inproceedings{48307, author = {{Habernal, Ivan and Gurevych, Iryna}}, booktitle = {{Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}}, publisher = {{Association for Computational Linguistics}}, title = {{{Which argument is more convincing? Analyzing and predicting convincingness of Web arguments using bidirectional LSTM}}}, doi = {{10.18653/v1/p16-1150}}, year = {{2016}}, }