An Evolutionary Algorithm Based on Replication Analysis for Bi-objective Feature Selection

Feature selection is a complicated optimization problem with significant practical applications. Nevertheless, its strength lies in significantly reducing the size of datasets and increasing classification efficiency. Several evolutionary algorithms (EAs) have been used to solve feature selection problems. However, most EAs are unsuitable to deal with real-world problems with multiple objectives. The multi-objective feature selection problems normally consist of two objectives: minimizing the number of feature selections and minimizing the classification errors. In this paper, a replication analysis method based on the evolutionary algorithm (RAEA) is proposed to classify bi-objective selected features. The proposed method has improved the framework of dominance-based EA from two viewpoints: firstly, modify the reproduction procedure to improve the features of offspring, and secondly, the replication analysis technique has been proposed to filter out unnecessary solutions. We conducted some experiments with the proposed method and compared it with five traditional MOEAs. The experimental results show that RAEA performs better results on most datasets, indicating that RAEA not only performs best in the optimization process but also performs better results in generalization and classification.