HBRFE: an enhanced recursive feature elimination model for big data classification

Abstract

The process of classification in big data is a tedious task due to the large number of volumes, veracity, and variety of the data. Classification of big data pave the path to organize the data and improve the classifier performance. This research article proposed a Hadoop framework based recursive feature elimination-based model called HBFRE for extract significant features from the big data by integrating map and reduce frame work. HBFRE extract the significant features by removing the least and irrelevant features from the dataset by using refined recursive feature elimination (RFE) with map and reduce framework. This method takes the mean of each attribute and find the variance in each instance. The proposed model is evaluated and analyzed by the accuracy performance and time complexity. This research utilized various classifier like artificial neural network (ANN), support vector machine (SVM), random forest (RF), k-nearest neighbors (KNN), and AdaBoost to measure the classification performance on the big data. Proposed HBRFE model is compared with different feature selection like RFE, relief, backwards feature elimination, maximum relevance k-nearest neighbors (MR-KNN), and scalable deep ensemble framework big data classification (SDELF-BDC).