Efficient Feature Selection Using Hybrid Slime Mould- Grey Wolf Optimization

Abstract

Feature selection becomes a prominent approach, especially when the records sets incorporate multiple variables and functions. It is the process of reducing the input data into an essential model, by disposing the unimportant variables and enhances the accuracy as well as the performance of type. In this paper Hybrid slime mould- grey wolf optimization algorithm is proposed for efficient feature selection by incorporating set of rules which could deal with the classical feature selection short comings. This algorithm is tested over prominent datasets with higher variety of distinct variables such as Diabetics, Alzheimer, Heart, Liver, Zoo, Breast Cancer. Four essential characteristics which makes feature selection is essential are; to simplify the model by way of lowering the range of parameters, subsequent to lower the training time, to lessen overfilling by using improving generalization, and to keep away from adding extra dimensionality. The proposed algorithm is compared with the state-of-the-art techniques Naïve Base (NB), support Vector Machines (SVM), K Nearest Neighbors (KNN), the best accuracy of the version is the exceptional classifier. Our experiments show case the comparative examine at distinct views. Furthermore, critical evaluation metrics Accuracy, Precision, Recall, F-Measure, Time, RMSE, MAE are used to evaluate the performance. Experimental consequences exhibits that SVM achieves a higher performance in all test corporations.