Cooperative Co-Evolution for Ensembles of Nested Dichotomies for Multi-Class Classification

MCML Authors

Marcel Wever

Dr.

* Former Member

→ Group Eyke Hüllermeier
 Artificial Intelligence and Machine Learning

Eyke Hüllermeier

Prof. Dr.

Principal Investigator

Artificial Intelligence and Machine Learning

Abstract

In multi-class classification, it can be beneficial to decompose a learning problem into several simpler problems. One such reduction technique is the use of so-called nested dichotomies, which recursively bisect the set of possible classes such that the resulting subsets can be arranged in the form of a binary tree, where each split defines a binary classification problem. Recently, a genetic algorithm for optimizing the structure of such nested dichotomies has achieved state-of-the-art results. Motivated by its success, we propose to extend this approach using a co-evolutionary scheme to optimize both the structure of nested dichotomies and their composition into ensembles through which they are evaluated. Furthermore, we present an experimental study showing this approach to yield ensembles of nested dichotomies at substantially lower cost and, in some cases, even with an improved generalization performance.

inproceedings

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GECCO 2023

Genetic and Evolutionary Computation Conference. Lisbon, Portugal, Jul 15-19, 2023.

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Authors

M. Wever • M. Özdogan • E. Hüllermeier

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Links

DOI

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Research Area

 A3 | Computational Models