Initialization of Feature Selection Search for Classification

Abstract

Selecting the best features in a dataset improves accuracy and efficiency of classifiers  in a learning process. Datasets generally have more features than necessary, some of  them being irrelevant or redundant to others. For this reason, numerous feature selection  methods have been developed, in which different evaluation functions and measures are  applied. This paper proposes the systematic application of individual feature evaluation  methods to initialize search-based feature subset selection methods. An exhaustive review  of the starting methods used by genetic algorithms from 2014 to 2020 has been carried out.  Subsequently, an in-depth empirical study has been carried out evaluating the proposal for  different search-based feature selection methods (Sequential forward and backward selection,  Las Vegas filter and wrapper, Simulated Annealing and Genetic Algorithms). Since  the computation time is reduced and the classification accuracy with the selected features  is improved, the initialization of feature selection proposed in this work is proved to be  worth considering while designing any feature selection algorithms.