A Novel Chaos Quasi-Oppositional based Flamingo Search Algorithm with Simulated Annealing for Feature Selection

Abstract

In present situations feature selection is one of the most vital tasks in machine learning. Diminishing the feature set helps to increase the accuracy of the classifier. Due to large number of information’s present in the dataset it is a tremendous process to select the necessary features from the dataset. So, to solve this problem a novel Chaos Quasi-Oppositional based Flamingo Search Algorithm with Simulated Annealing algorithm (CQOFSA-SA) is proposed for feature selection and to select the optimal feature set from the datasets and thus it shrinks the dimension of the dataset. The FSA approach is used to choose the optimal feature subset from the dataset. In each iteration, the optimal solution of FSA is enriched by Simulated Annealing (SA). TheChaos Quasi-Oppositional based learning (CQOBL) included in the initialization of FSA improves the convergence rate and increases the searching capability of the FSA approach in choosing the optimal feature set. From the experimental outcomes, it is proved that the proposed CQOFSA-SA outperforms other feature selection approaches in terms of accuracy, optimal reduced feature set, fast convergence and fitness value.