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Foto: Universität Paderborn, Adelheid Rutenburges

Alexander Tornede

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Publikationen
 Alexander Tornede

Intelligente Systeme und Maschinelles Lernen

Mitglied - Wissenschaftlicher Mitarbeiter

Sonderforschungsbereich 901

Mitglied - Wissenschaftlicher Mitarbeiter

Telefon:
+49 5251 60-3345
Büro:
O4.164
Besucher:
Pohlweg 51
33098 Paderborn


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