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Learning Interpretable Rules for Multi-label Classification
Eneldo Loza Mencía, Johannes Fürnkranz, Eyke Hüllermeier +1
Multi-label classification (MLC) is a supervised learning problem in which, contrary to standard multiclass classification, an instance can be associated with several class labels…
Automated Multi-Label Classification based on ML-Plan
Marcel Wever, Felix Mohr, Eyke Hüllermeier
Automated machine learning (AutoML) has received increasing attention in the recent past. While the main tools for AutoML, such as Auto-WEKA, TPOT, and auto-sklearn, mainly deal wi…
Multi-Target Prediction: A Unifying View on Problems and Methods
Willem Waegeman, Krzysztof Dembczynski, Eyke Huellermeier
Multi-target prediction (MTP) is concerned with the simultaneous prediction of multiple target variables of diverse type. Due to its enormous application potential, it has develope…
Automated Machine Learning Service Composition
Felix Mohr, Marcel Wever, Eyke Hüllermeier
Automated service composition as the process of creating new software in an automated fashion has been studied in many different ways over the last decade. However, the impact of a…
Preference-based Online Learning with Dueling Bandits: A Survey
Viktor Bengs, Robert Busa-Fekete, Adil El Mesaoudi-Paul +1
In machine learning, the notion of multi-armed bandits refers to a class of online learning problems, in which an agent is supposed to simultaneously explore and exploit a given se…
Mining Rank Data
Sascha Henzgen, Eyke Hüllermeier
The problem of frequent pattern mining has been studied quite extensively for various types of data, including sets, sequences, and graphs. Somewhat surprisingly, another important…
Deep Architectures for Learning Context-dependent Ranking Functions
Karlson Pfannschmidt, Pritha Gupta, Eyke Hüllermeier
Object ranking is an important problem in the realm of preference learning. On the basis of training data in the form of a set of rankings of objects, which are typically represent…