Feature Selection With the Whale Optimization Algorithm and Artificial Neural Network

Abstract

Feature selection is addressed an important problem in data mining. To be high dimension of the data obtained from the sources is encountered as an issue in many issues such as computation cost. For this reason, eliminating the unnecessary ones among these data and choosing the appropriate ones makes it possible to evaluate the information correctly. In this study, it is tried to suggest a method that can be used in feature selection on data sets. In this method, The Whale Optimization Algorithm, which is one of the new meta-heuristic algorithms, is used to select appropriate features. Training with artificial neural networks takes place during the evaluation process of selected features. At the end of the training, the features that provide the minimum error value are selected. In the performance evaluation of the method, known data sets will be used and the results will be given in comparison with the Particle Swarm Optimization method. © 2017 IEEE.