Feature selection optimized by the artificial immune algorithm based on genome shuffling and conditional lethal mutation

Improving classification performance is an essential goal for various practical applications. Feature selection has become an important data preprocessing step in machine learning systems. However, many effective methods based on heuristic search strategies have the problem of high running costs. This paper proposes an efficient multiobjective feature selection method based on artificial immune algorithm optimization. It introduces a clone selection algorithm to explore the search space of optimal feature subsets. According to the target requirements of feature selection, combined with biological research results, this method introduces genome shuffling technology and a conditional lethal mutation mechanism to improve the search performance of the algorithm. Experimental comparisons are conducted on 21 benchmark datasets with 17 advanced feature selection methods in terms of classification accuracy, the number of feature subsets, and computational cost. The results show that the algorithm achieves the smallest number of selected features (only 3.26% compared to the lowest) and better average classification accuracy with a much lower average computational cost than others (only 3.62% compared to the lowest).

Automated summary

Improving classification performance is crucial for practical applications, and feature selection is an important preprocessing step in machine learning systems. However, existing methods based on heuristic search strategies often have high running costs. This paper proposes an efficient feature selection method based on artificial immune algorithm optimization, which introduces a clone selection algorithm and genetic shuffling technology to improve search performance. Experimental results show that this method achieves better classification accuracy with fewer selected features and lower computational cost compared to other methods.