Feature Selection and Classification of Big Data Using MapReduce Framework

The Feature selection (FS) plays an imperative role in Machine Learning (ML) but it is really demanding when we apply feature selection to voluminous data. The conventional FS methods are not competent in handling big datasets. This leads to the need of a technology that processes the data in parallel. MapReduce is a new programming framework used for processing massive data by using the "divide and conquer" approach. In this paper, a novel parallel BAT algorithm is proposed for feature selection of big datasets and finally classification is applied to the set of known classifiers. The proposed parallel FS technique is highly scalable for big datasets. The experimental results have shown improved efficacy of the proposed algorithm in terms of the accuracy and comparatively lesser execution time when the number of parallel nodes is increased.