Hybrid Binary Dragonfly Optimization Algorithm with Statistical Dependence for Feature Selection

Abstract

The aim of the feature selection technique is to obtain the most important information from a specific set of datasets. Further elaborations in the feature selection technique will positively affect the classification process, which can be applied in various areas such as machine learning, pattern recognition, and signal processing. In this study, a hybrid algorithm between the binary dragonfly algorithm (BDA) and the statistical dependence (SD) is presented, whereby the feature selection method in discrete space is modeled as a binary-based optimization algorithm, guiding BDA and using the accuracy of the k-nearest neighbors classifier on the dataset to verify it in the chosen fitness function. The experimental results demonstrated that the proposed algorithm, which we refer to as SD-BDA, outperforms other algorithms in terms of the accuracy of the results represented by the cost of the calculations and the accuracy of the classification.