An efficient feature selection using artificial fish swarm optimization and svm classifier

The medical datasets have many features if the features have a tendency of mutation then the risk of disease increases which makes difficult to provide a diagnosis of disease. In the dataset, every feature is a contributor for prediction accuracy, the selection of significant features from the dataset is a challenging task. The feature selection technique based on metaheuristic algorithms is used for the selection of significant data. The metaheuristic algorithm inspired by the fish behavior under water is artificial fish swarm optimization (AFSO) is proposed in this paper. The wrapper approach of AFSO with support vector machine (SVM) is used for finding the feature subset with a minimum number of features. The proposed approach is tested on nine different datasets having binary and multiple imbalanced classes and correlated with other metaheuristic algorithms. The results show that the proposed approach is providing high classification accuracy with features subset having fewer features.