A joint multiobjective optimization of feature selection and classifier design for high-dimensional data classification

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 al-gorithms 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.

Automated summary

Feature selection has been extensively studied in data mining and machine learning. Meta-heuristic algorithms are commonly used to solve feature selection problems, however, they suffer from issues such as large search space and long computation time. This article proposes a joint multiobjective optimization method, called JMO-FSCD, for feature selection and classifier design. The proposed approach uses a neural network as a classifier and introduces a non-iterative algorithm for training the classifier. Experimental results demonstrate the superior performance of JMO-FSCD compared to six state-of-the-art feature selection algorithms.