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Paderborn University in spring. Show image information

Paderborn University in spring.

Photo: Paderborn University, Kamil Glabica.

Alexander Tornede

 Alexander Tornede

Intelligent Systems and Machine Learning

Member - Research Assistant

Sonderforschungsbereich 901

Member - Research Assistant

+49 5251 60-3352
Pohlweg 51
33098 Paderborn

Open list in Research Information System


Extreme Algorithm Selection with Dyadic Feature Representation

A. Tornede, M.D. Wever, E. Hüllermeier, in: Discovery Science, 2020

Hybrid Ranking and Regression for Algorithm Selection

J.M. Hanselle, A. Tornede, M.D. Wever, E. Hüllermeier, in: KI 2020: Advances in Artificial Intelligence, 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.

AutoML for Predictive Maintenance: One Tool to RUL them all

T. Tornede, A. Tornede, M.D. Wever, F. Mohr, E. Hüllermeier, in: Proceedings of the ECMLPKDD 2020, 2020

Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis

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

Algorithm selection (AS) deals with the automatic selection of an algorithm from a fixed set of candidate algorithms most suitable for a specific instance of an algorithmic problem class, where "suitability" often refers to an algorithm's runtime. Due to possibly extremely long runtimes of candidate algorithms, training data for algorithm selection models is usually generated under time constraints in the sense that not all algorithms are run to completion on all instances. Thus, training data usually comprises censored information, as the true runtime of algorithms timed out remains unknown. However, many standard AS approaches are not able to handle such information in a proper way. On the other side, survival analysis (SA) naturally supports censored data and offers appropriate ways to use such data for learning distributional models of algorithm runtime, as we demonstrate in this work. We leverage such models as a basis of a sophisticated decision-theoretic approach to algorithm selection, which we dub Run2Survive. Moreover, taking advantage of a framework of this kind, we advocate a risk-averse approach to algorithm selection, in which the avoidance of a timeout is given high priority. In an extensive experimental study with the standard benchmark ASlib, our approach is shown to be highly competitive and in many cases even superior to state-of-the-art AS approaches.


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

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.


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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