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

verfasst von

Marcel Wever, Miran Özdogan, Eyke Hüllermeier

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.

Externe Organisation(en)

Munich Center for Machine Learning (MCML)

Typ

Aufsatz in Konferenzband

Seiten

597-605

Anzahl der Seiten

9

Publikationsdatum

2023

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Software, Artificial intelligence, Theoretische Informatik

Elektronische Version(en)

https://doi.org/10.1145/3583131.3590457 (Zugang: Geschlossen)