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- W4316655441 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." @default.
- W4316655441 created "2023-01-17" @default.
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- W4316655441 date "2023-05-01" @default.
- W4316655441 modified "2023-10-16" @default.
- W4316655441 title "A joint multiobjective optimization of feature selection and classifier design for high-dimensional data classification" @default.
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- W4316655441 doi "https://doi.org/10.1016/j.ins.2023.01.069" @default.
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