Feature Selection Algorithm Based on Mutual Information and Genetic Algorithm
A novel feature selection algorithm using mutual information and genetic algorithm is presented in this paper.The algo-rithm designed the metrics for measuring the correlation between features and that between features and classes based on mutual in-formation.By combining the strong global optimization capability of genetic algorithms,it can search for a globally optimal feature subset in the candidate feature space,characterized by low inter-feature correlation,high feature-to-class correlation,and high classi-fication accuracy.In this paper,comparative experiments were conducted on 10 standard datasets using 8 correlation-based feature selection algorithms.Under 3 classifiers,the algorithm proposed in this paper achieves average classification accuracies of 88.98%,87.5%,and 86.95%,respectively,outperforming all the comparative algorithms.The experimental outcomes demonstrate the effec-tiveness of the proposed algorithm in significantly reducing the dimensionality of the original feature sets while enhancing the accu-racies of classifiers.
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