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Memetic Computing

Erschienen in:

01.03.2016 | Regular Research Paper

Convex hull-based multi-objective evolutionary computation for maximizing receiver operating characteristics performance

verfasst von: Wenjing Hong, Ke Tang

Erschienen in: Memetic Computing | Ausgabe 1/2016

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Abstract

The receiver operating characteristics (ROC) analysis has gained increasing popularity for analyzing the performance of classifiers. In particular, maximizing the convex hull of a set of classifiers in the ROC space, namely ROCCH maximization, is becoming an increasingly important problem. In this work, a new convex hull-based evolutionary multi-objective algorithm named ETriCM is proposed for evolving neural networks with respect to ROCCH maximization. Specially, convex hull-based sorting with convex hull of individual minima (CH-CHIM-sorting) and extreme area extraction selection (EAE-selection) are proposed as a novel selection operator. Empirical studies on 7 high-dimensional and imbalanced datasets show that ETriCM outperforms various state-of-the-art algorithms including convex hull-based evolutionary multi-objective algorithm (CH-EMOA) and non-dominated sorting genetic algorithm II (NSGA-II).

Vorheriger Artikel Differential evolution framework for big data optimization

Nächster Artikel A multi-objective memetic algorithm based on decomposition for big optimization problems

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Titel: Convex hull-based multi-objective evolutionary computation for maximizing receiver operating characteristics performance
verfasst von: Wenjing Hong
Ke Tang
Publikationsdatum: 01.03.2016
Verlag: Springer Berlin Heidelberg
Erschienen in: Memetic Computing / Ausgabe 1/2016
Print ISSN: 1865-9284
Elektronische ISSN: 1865-9292
DOI: https://doi.org/10.1007/s12293-015-0176-8

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