An Assessment of Nature-Inspired Algorithms for Text Feature Selection

Abstract

This paper provides a comprehensive assessment of feature selection (FS) methods that are originated from nature-inspired (NI) meta-heuristics, where two well-known filter-based FS methods are also included for comparison. The performances of the considered methods are compared on two different high-dimensional and real-world text datasets against the accuracy, the number of selected features, and computation time. This study differs from existing studies in terms of the extent of experimental analyses performed under different circumstances where classifier, feature model, and term weighting scheme are different. The results of the extensive experiments indicate that NI algorithms produce slightly different results than filter-based methods for the problem of the text FS. However, filter-based methods often provide better results by using a lower number of features and computation times.