Alexander Tornede
University of Paderborn
31 Papers
46 Citations
Alexander Tornede is an academic researcher from University of Paderborn. The author has contributed to research in topics: Computer science & Selection (genetic algorithm). The author has an hindex of 5, co-authored 20 publications.
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Papers
AutoML for Multi-Label Classification: Overview and Empirical Evaluation
TL;DR: In this paper, the authors survey existing approaches to AutoML for multi-label classification (MLC) and propose a benchmarking framework that supports a fair and systematic comparison of these approaches.
A Survey of Methods for Automated Algorithm Configuration
Elias Arnold Schede,Jasmin Brandt,Alexander Tornede,Marcel Dominik Wever,Viktor Bengs,E. Hullermeier,Kevin Tierney +6 more
TL;DR: A review of existing AC literature within the lens of taxonomies, which outlines relevant design choices of configuration approaches, contrast methods and problem variants against each other, and provides a look at future research directions in the field of AC.
Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning
TL;DR: In this paper, the authors present an approach to predict the runtime of two-step machine learning pipelines with up to one pre-processor, which can be used to anticipate whether or not a pipeline will time out.
42
Extreme Algorithm Selection With Dyadic Feature Representation
TL;DR: This work assesses the applicability of state-of-the-art AS techniques to the XAS setting and proposes approaches leveraging a dyadic feature representation in which both problem instances and algorithms are described, finding the latter to improve significantly over the current state of the art in various metrics.
AutoML for Predictive Maintenance: One Tool to RUL Them All
Tanja Tornede,Alexander Tornede,Marcel Dominik Wever,Felix Mohr,Eyke Hüllermeier +4 more
- 14 Sep 2020
TL;DR: In this article, an adaptation of the AutoML tool ML-Plan to the problem of RUL estimation is presented, combining feature engineering, algorithm selection, and hyperparameter optimization into an endto-end approach.
20