Assessment of Nature-Inspired Algorithms for Text Feature Selection

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