Feature selection with modified lion's algorithms and support vector machine for high-dimensional data

Abstract The lion’s algorithm is a novel evolutionary algorithm designed to imitate the behavior observed in a pride of lions. This study solves the classification problem by employing the lion’s algorithm for the selection of feature subsets in high dimensional data. The proposed feature selection process identifies and removes irrelevant/redundant features to reduce data dimensionality and thereby improve the efficiency and accuracy of classification. We devised three versions of the lion’s algorithm in which greedy search was applied to the territorial defense strategy and/or territorial takeover strategy. Experiments using datasets in the UCI machine learning database demonstrate the superiority of the modified versions over the original algorithm. Ultimately, the approach involving the application of greedy search to territorial defense proved the most effective.

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