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A joint multiobjective optimization of feature selection and classifier design for high-dimensional data classification.
- Source :
-
Information Sciences . May2023, Vol. 626, p457-473. 17p. - Publication Year :
- 2023
-
Abstract
- Feature selection (FS) in data mining and machine learning has attracted extensive attention. The purpose of FS in a classification task is to find the optimal subset of features from given candidate features. Recently, more and more meta-heuristic algorithms have been used to deal with the FS problems. However, meta-heuristic algorithms suffer from certain issues, such as large search space for solutions and huge time consumption. Moreover, most of existing meta-heuristic algorithms focus only on the selection of an optimal feature subset, and pay little attention to the optimal design of the classifier. In this article, we propose a joint multiobjective optimization method for both feature selection and classifier design, called JMO-FSCD. The proposed approach uses neural network as a classifier and introduces a non-iterative algorithm for training the classifier so as to ensure good performance and fast learning. A new coding scheme is also designed for optimizing FS and classifier simultaneously. For demonstrating the superiority of the proposed approach, its performance is compared with those of six state-of-the-art FS algorithms. Experimental results on thirty-five benchmark data sets reflect the superior performance of the proposed JMO-FSCD. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00200255
- Volume :
- 626
- Database :
- Academic Search Index
- Journal :
- Information Sciences
- Publication Type :
- Periodical
- Accession number :
- 162503791
- Full Text :
- https://doi.org/10.1016/j.ins.2023.01.069