TY - CONF AU - Jasinska, K. AU - Dembczynski, K. AU - Busa-Fekete, Robert AU - Klerx, Timo AU - Hüllermeier, Eyke ED - Balcan, M.F. ED - Weinberger, K.Q. ID - 10222 T2 - Proceedings ICML-2016, 33th International Conference on Machine Learning, New York, USA TI - Extreme F-measure maximization using sparse probability estimates ER - TY - CONF AU - Melnikov, Vitaly AU - Hüllermeier, Eyke ID - 10223 T2 - European Conference on Machine Learning and Knowledge Discovery in Databases, Part II, Riva del Garda, Italy TI - Learning to aggregate using uninorms, in Proceedings ECML/PKDD-2016 ER - TY - CONF AU - Dembczynski, K. AU - Kotlowski, W. AU - Waegeman, W. AU - Busa-Fekete, Robert AU - Hüllermeier, Eyke ID - 10224 T2 - In Proceedings ECML/PKDD European Conference on Maschine Learning and Knowledge Discovery in Databases, Part II, Riva del Garda, Italy TI - Consistency of probalistic classifier trees ER - TY - CONF AU - Shabani, Aulon AU - Paul, Adil AU - Platon, R. AU - Hüllermeier, Eyke ID - 10225 T2 - In Proceedings ICCBR, 24th International Conference on Case-Based Reasoning, Atlanta, GA, USA TI - Predicting the electricity consumption of buildings: An improved CBR approach ER - TY - CONF AU - Pfannschmidt, Karlson AU - Hüllermeier, Eyke AU - Held, S. AU - Neiger, R. ID - 10226 T2 - In Proceedings IPMU 16th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Part 1, Eindhoven, The Netherlands TI - Evaluating tests in medical diagnosis-Combining machine learning with game-theoretical concepts ER - TY - CONF AU - Labreuche, C. AU - Hüllermeier, Eyke AU - Vojtas, P. AU - Fallah Tehrani, A. ED - Busa-Fekete, Robert ED - Hüllermeier, Eyke ED - Mousseau, V. ED - Pfannschmidt, Karlson ID - 10227 T2 - Proceedings DA2PL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning TI - On the Identifiability of models in multi-criteria preference learning ER - TY - CONF AU - Schäfer, Dirk AU - Hüllermeier, Eyke ED - Busa-Fekete, Robert ED - Hüllermeier, Eyke ED - Mousseau, V. ED - Pfannschmidt, Karlson ID - 10228 T2 - Proceedings DA2PL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning TI - Preference-Based Reinforcement Learning Using Dyad Ranking ER - TY - CONF AU - Couso, Ines AU - Ahmadi Fahandar, Mohsen AU - Hüllermeier, Eyke ED - Busa-Fekete, Robert ED - Hüllermeier, Eyke ED - Mousseau, V. ED - Pfannschmidt, Karlson ID - 10229 T2 - Proceedings DA2PL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning TI - Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators ER - TY - CONF AU - Lu, S. AU - Hüllermeier, Eyke ED - Hoffmann, F. ED - Hüllermeier, Eyke ED - Mikut, R. ID - 10230 T2 - Proceedings 26. Workshop Computational Intelligence, KIT Scientific Publishing TI - Support vector classification on noisy data using fuzzy supersets losses ER - TY - CONF AU - Schäfer, Dirk AU - Hüllermeier, Eyke ID - 10231 T2 - In Workshop LWDA "Lernen, Wissen, Daten, Analysen" TI - Plackett-Luce networks for dyad ranking ER - TY - GEN ED - Kaminka, G.A. ED - Fox, M. ED - Bouquet, P. ED - Hüllermeier, Eyke ED - Dignum, V. ED - Dignum, F. ED - van Harmelen, F. ID - 10263 TI - ECAI 2016, 22nd European Conference on Artificial Intelligence, including PAIS 2016, Prestigious Applications of Artificial Intelligence VL - 285 ER - TY - JOUR AU - Leinweber, M. AU - Fober, T. AU - Strickert, M. AU - Baumgärtner, L. AU - Klebe, G. AU - Freisleben, B. AU - Hüllermeier, Eyke ID - 10264 IS - 6 JF - IEEE Transactions on Knowledge and Data Engineering TI - CavSimBase: A database for large scale comparison of protein binding sites VL - 28 ER - TY - JOUR AU - Riemenschneider, M. AU - Senge, Robin AU - Neumann, U. AU - Hüllermeier, Eyke AU - Heider, D. ID - 10266 IS - 10 JF - BioData Mining TI - Exploiting HIV-1 protease and reverse transcriptase cross-resistance information for improved drug resistance prediction by means of multi-label classification VL - 9 ER - TY - CONF AB - The Collaborative Research Centre "On-The-Fly Computing" works on foundations and principles for the vision of the Future Internet. It proposes the paradigm of On-The-Fly Computing, which tackles emerging worldwide service markets. In these markets, service providers trade software, platform, and infrastructure as a service. Service requesters state requirements on services. To satisfy these requirements, the new role of brokers, who are (human) actors building service compositions on the fly, is introduced. Brokers have to specify service compositions formally and comprehensively using a domain-specific language (DSL), and to use service matching for the discovery of the constituent services available in the market. The broker's choice of the DSL and matching approaches influences her success of building compositions as distinctive properties of different service markets play a significant role. In this paper, we propose a new approach of engineering a situation-specific DSL by customizing a comprehensive, modular DSL and its matching for given service market properties. This enables the broker to create market-specific composition specifications and to perform market-specific service matching. As a result, the broker builds service compositions satisfying the requester's requirements more accurately. We evaluated the presented concepts using case studies in service markets for tourism and university management. AU - Arifulina, Svetlana AU - Platenius, Marie Christin AU - Mohr, Felix AU - Engels, Gregor AU - Schäfer, Wilhelm ID - 280 T2 - Proceedings of the IEEE 11th World Congress on Services (SERVICES), Visionary Track: Service Composition for the Future Internet TI - Market-Specific Service Compositions: Specification and Matching ER - TY - JOUR AB - On-the-fly composition of service-based software solutions is still a challenging task. Even more challenges emerge when facing automatic service composition in markets of composed services for end users. In this paper, we focus on the functional discrepancy between “what a user wants” specified in terms of a request and “what a user gets” when executing a composed service. To meet the challenge of functional discrepancy, we propose the combination of existing symbolic composition approaches with machine learning techniques. We developed a learning recommendation system that expands the capabilities of existing composition algorithms to facilitate adaptivity and consequently reduces functional discrepancy. As a representative of symbolic techniques, an Artificial Intelligence planning based approach produces solutions that are correct with respect to formal specifications. Our learning recommendation system supports the symbolic approach in decision-making. Reinforcement Learning techniques enable the recommendation system to adjust its recommendation strategy over time based on user ratings. We implemented the proposed functionality in terms of a prototypical composition framework. Preliminary results from experiments conducted in the image processing domain illustrate the benefit of combining both complementary techniques. AU - Jungmann, Alexander AU - Mohr, Felix ID - 323 IS - 1 JF - Journal of Internet Services and Applications TI - An approach towards adaptive service composition in markets of composed services ER - TY - CONF AB - Services are self-contained software components that can beused platform independent and that aim at maximizing software reuse. Abasic concern in service oriented architectures is to measure the reusabilityof services. One of the most important qualities is the functionalreusability, which indicates how relevant the task is that a service solves.Current metrics for functional reusability of software, however, have verylittle explanatory power and do not accomplish this goal.This paper presents a new approach to estimate the functional reusabilityof services based on their relevance. To this end, it denes the degreeto which a service enables the execution of other services as its contri-bution. Based on the contribution, relevance of services is dened as anestimation for their functional reusability. Explanatory power is obtainedby normalizing relevance values with a reference service. The applicationof the metric to a service test set conrms its supposed capabilities. AU - Mohr, Felix ID - 324 T2 - Proceedings of the 14th International Conference on Software Reuse (ICSR) TI - A Metric for Functional Reusability of Services ER - TY - CONF AB - Services are self-contained and platform independent software components that aim at maximizing software reuse. The automated composition of services to a target software artifact has been tackled with many AI techniques, but existing approaches make unreasonably strong assumptions such as a predefined data flow, are limited to tiny problem sizes, ignore non-functional properties, or assume offline service repositories. This paper presents an algorithm that automatically composes services without making such assumptions. We employ a backward search algorithm that starts from an empty composition and prepends service calls to already discovered candidates until a solution is found. Available services are determined during the search process. We implemented our algorithm, performed an experimental evaluation, and compared it to other approaches. AU - Mohr, Felix AU - Jungmann, Alexander AU - Kleine Büning, Hans ID - 319 T2 - Proceedings of the 12th IEEE International Conference on Services Computing (SCC) TI - Automated Online Service Composition ER - TY - JOUR AU - Senge, Robin AU - Hüllermeier, Eyke ID - 4792 IS - 6 JF - IEEE Transactions on Fuzzy Systems SN - 1063-6706 TI - Fast Fuzzy Pattern Tree Learning for Classification VL - 23 ER - TY - CONF AU - Schäfer, D. AU - Hüllermeier, Eyke ID - 15406 T2 - in Proceedings of the 2015 international Workshop on Meta-Learning and Algorithm Selection co-located ECML/PKDD, Porto, Portugal TI - Preference-based meta-learning using dyad ranking: Recommending algorithms in cold-start situations ER - TY - CONF AU - Paul, Adil AU - Hüllermeier, Eyke ID - 15749 T2 - In Workshop Proceedings from ICCBR, 23rd International Conference on Case-Based Reasoning, Frankfurt, Germany TI - A cbr approach to the angry birds game ER -