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Die Universität am Abend. Bildinformationen anzeigen

Die Universität am Abend.

Foto: Universität Paderborn, Kamil Glabica

Alexander Tornede

Kontakt
Publikationen
 Alexander Tornede

Intelligente Systeme und Maschinelles Lernen

Mitglied - Wissenschaftlicher Mitarbeiter

Sonderforschungsbereich 901

Mitglied - Wissenschaftlicher Mitarbeiter

Telefon:
+49 5251 60-3352
Büro:
O4.149
Besucher:
Pohlweg 51
33098 Paderborn


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2020

LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification

M.D. Wever, A. Tornede, F. Mohr, E. Hüllermeier, Springer, 2020

In multi-label classification (MLC), each instance is associated with a set of class labels, in contrast to standard classification where an instance is assigned a single label. Binary relevance (BR) learning, which reduces a multi-label to a set of binary classification problems, one per label, is arguably the most straight-forward approach to MLC. In spite of its simplicity, BR proved to be competitive to more sophisticated MLC methods, and still achieves state-of-the-art performance for many loss functions. Somewhat surprisingly, the optimal choice of the base learner for tackling the binary classification problems has received very little attention so far. Taking advantage of the label independence assumption inherent to BR, we propose a label-wise base learner selection method optimizing label-wise macro averaged performance measures. In an extensive experimental evaluation, we find that or approach, called LiBRe, can significantly improve generalization performance.


2019

Automating Multi-Label Classification Extending ML-Plan

M.D. Wever, F. Mohr, A. Tornede, E. Hüllermeier, 2019

Existing tools for automated machine learning, such as Auto-WEKA, TPOT, auto-sklearn, and more recently ML-Plan, have shown impressive results for the tasks of single-label classification and regression. Yet, there is only little work on other types of machine learning problems so far. In particular, there is almost no work on automating the engineering of machine learning solutions for multi-label classification (MLC). We show how the scope of ML-Plan, an AutoML-tool for multi-class classification, can be extended towards MLC using MEKA, which is a multi-label extension of the well-known Java library WEKA. The resulting approach recursively refines MEKA's multi-label classifiers, nesting other multi-label classifiers for meta algorithms and single-label classifiers provided by WEKA as base learners. In our evaluation, we find that the proposed approach yields strong results and performs significantly better than a set of baselines we compare with.


Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking

A. Tornede, M.D. Wever, E. Hüllermeier, in: Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019, KIT Scientific Publishing, Karlsruhe, 2019, pp. 135-146


Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking

A. Hetzer, M.D. Wever, F. Mohr, E. Hüllermeier. Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking. 2019.


From Automated to On-The-Fly Machine Learning

F. Mohr, M.D. Wever, A. Tornede, E. Hüllermeier. From Automated to On-The-Fly Machine Learning. 2019.


Towards Automated Machine Learning for Multi-Label Classification

M.D. Wever, F. Mohr, E. Hüllermeier, A. Hetzer. Towards Automated Machine Learning for Multi-Label Classification. 2019.


2017

jPL: A Java-based Software Framework for Preference Learning

P. Gupta, A. Hetzer, T. Tornede, S. Gottschalk, A. Kornelsen, S. Osterbrink, K. Pfannschmidt, E. Hüllermeier. jPL: A Java-based Software Framework for Preference Learning. 2017.



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