Multiobjective optimization algorithm with dynamic operator selection for feature selection in high-dimensional classification

Feature selection (FS) is an important technique in data preprocessing that aims to reduce the number of features for training while maintaining a high accuracy for classification. In recent studies, FS has been extended to optimize multiple objectives simultaneously in classification. To better sol...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Applied soft computing Jg. 143; S. 110360
Hauptverfasser: Wei, Wenhong, Xuan, Manlin, Li, Lingjie, Lin, Qiuzhen, Ming, Zhong, Coello Coello, Carlos A.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.08.2023
Schlagworte:
Evolutionary algorithm
Feature selection
High-dimensional classification
Multiobjective optimization
Resource allocation
Feature selection
High-dimensional classification
Multiobjective optimization
Evolutionary algorithm
Resource allocation
ISSN:1568-4946
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Feature selection (FS) is an important technique in data preprocessing that aims to reduce the number of features for training while maintaining a high accuracy for classification. In recent studies, FS has been extended to optimize multiple objectives simultaneously in classification. To better solve this problem, this paper proposes a new multiobjective optimization algorithm with dynamic operator selection for feature selection in high-dimensional classification, called FS-DOS. First, two complementary search operators with different characteristics are designed, where the first operator is a quick search (QS) operator aiming to accelerate the convergence speed, and the other operator is a modified binary differential evolution (BDE) operator that can prevent the algorithm from falling into a local optimum. In addition, a dynamic selection strategy based on the idea of resource allocation is also designed to dynamically select the most suitable operator for each solution according to its corresponding performance improvement on aggregated objective values. The simulation results on fifteen different real-world high-dimensional FS datasets show that FS-DOS can obtain a feature subset with higher quality than several state-of-the-art FS algorithms. Importantly, in terms of error rate, FS-DOS wins 55 out of 75 comparisons. In terms of dimensionality reduction, the number of features selected by FS-DOS is between one hundredth and one thousandth of the original dataset. •This paper proposes an effective evolutionary algorithm with dynamic operator selection strategy for high-dimensional feature selection.•The QS operator is used to select the most important features for accelerating the convergence, and the BDE operator with strong exploration ability is designed to avoid local optimum.•The proposed method presents is superior to state-of-the-art FS methods on 15 real-world medical high-dimensional datasets.
ISSN:1568-4946
DOI:10.1016/j.asoc.2023.110360