Parallel Feature Subset Selection Wrappers Using k-means Classifier

Abstract

In a world where the volume of data is constantly increasing, the implementation time of various processes increases significantly. Therefore, the proper management and the effort to reduce the dimensions of the datasets are considered imperative. Feature selection can reduce the size of the datasets by keeping a smaller subset, while improving the accuracy of the classification. The main purpose of this paper is to propose and examine the efficiency of parallel feature selection wrappers based on k-means classifier. The simple kmeans algorithm and a parallel version of it are used. Different parallelization variants of feature subset selection (fss) are presented and their accuracy and computation time are also evaluated on four different datasets. The comparison is performed among different parallelization variations and the serial implementation of fss with the k-means clustering algorithm. Finally, the results of the research are presented, highlighting the importance of parallelization in reducing the execution time of the proposed algorithms.