Ensembles of evolved nested dichotomies for classification

verfasst von

Marcel Wever, Felix Mohr, Eyke Hüllermeier

Abstract

In multinomial classification, reduction techniques are commonly used to decompose the original learning problem into several simpler problems. For example, by recursively bisecting the original set of classes, so-called nested dichotomies define a set of binary classification problems that are organized in the structure of a binary tree. In contrast to the existing one-shot heuristics for constructing nested dichotomies and motivated by recent work on algorithm configuration, we propose a genetic algorithm for optimizing the structure of such dichotomies. A key component of this approach is the proposed genetic representation that facilitates the application of standard genetic operators, while still supporting the exchange of partial solutions under recombination. We evaluate the approach in an extensive experimental study, showing that it yields classifiers with superior generalization performance.

Externe Organisation(en)

Heinz Nixdorf Institut (HNI)
Universität Paderborn

Typ

Aufsatz in Konferenzband

Seiten

561-568

Anzahl der Seiten

8

Publikationsdatum

02.07.2018

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Angewandte Informatik, Software, Theoretische Informatik und Mathematik

Elektronische Version(en)

https://doi.org/10.1145/3205455.3205562 (Zugang: Geschlossen)