Feature Selection via Pareto Multi-objective Genetic Algorithms

Feature selection, an important combinatorial optimization problem in data mining, aims to find a reduced subset of features of high quality in a dataset. Different categories of importance measures can be used to estimate the quality of a feature subset. Since each measure provides a distinct persp...

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Veröffentlicht in:Applied artificial intelligence Jg. 31; H. 9-10; S. 764 - 791
Hauptverfasser: Spolaôr, Newton, Lorena, Ana Carolina, Diana Lee, Huei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Philadelphia Taylor & Francis 26.11.2017
Taylor & Francis Ltd
Taylor & Francis Group
Schlagworte:
Combinatorial analysis
Data mining
Genetic algorithms
Multiple objective analysis
Pareto optimization
Performance prediction
ISSN:0883-9514, 1087-6545
Online-Zugang:Volltext
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Zusammenfassung:Feature selection, an important combinatorial optimization problem in data mining, aims to find a reduced subset of features of high quality in a dataset. Different categories of importance measures can be used to estimate the quality of a feature subset. Since each measure provides a distinct perspective of data and of which are their important features, in this article we investigate the simultaneous optimization of importance measures from different categories using multi-objective genetic algorithms grounded in the Pareto theory. An extensive experimental evaluation of the proposed method is presented, including an analysis of the performance of predictive models built using the selected subsets of features. The results show the competitiveness of the method in comparison with six feature selection algorithms. As an additional contribution, we conducted a pioneer, rigorous, and replicable systematic review on related work. As a result, a summary of 93 related papers strengthens features of our method.
Bibliographie:ObjectType-Article-1
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ISSN:0883-9514
1087-6545
DOI:10.1080/08839514.2018.1444334