Feature reduction approach for classification of datasets based on genetic algorithm

Purpose This study aims to propose a genetic algorithm (GA)-based feature reduction (FR) approach applicable to any classification data set, regardless of domain, dimensionality and size of the data set, with any number of classes and data format. Design/methodology/approach Two levels of experiments were conducted to evaluate the efficacy of the proposed model. Level-I focuses on assessing the reduction percentage of features, classification accuracy (CA) and learning time (LT) using the J48 decision tree learner. Level-II aims to examine the improvement in performance metrics – specifically CA, LT and the maximum achieved CA – across 18 data sets, using four diverse and competitive classifiers, namely, naïve Bayes, artificial neural network, K-nearest neighbour and random forest, both with and without FR. Findings The proposed GA-based model achieves a significant reduction in the number of features across the data sets. Furthermore, it leads to a noticeable decrease in LT and an improvement in CA across various classifiers on the refined data sets. Originality/value Experimental results demonstrate that the proposed model is scalable and performs at par with other specialized FR techniques.