Journal Article

Enhancing Big Data Feature Selection Using a Hybrid Correlation-Based Feature Selection

Hamido Fujita, Masurah Mohamad, Ali Selamat, Ruben Gonzalez Crespo, Ondrej Krejcar, Enrique Herrera-Viedma

2021

Overview

This study proposes an alternate data extraction method that combines three well-known feature selection methods for handling large and problematic datasets: the correlation-based feature selection (CFS), best first search (BFS), and dominance-based rough set approach (DRSA) methods. This study aims to enhance the classifier’s performance in decision analysis by eliminating uncorrelated and inconsistent data values. The proposed method, named CFS-DRSA, comprises several phases executed in sequence, with the main phases incorporating two crucial feature extraction tasks. Data reduction is first, which implements a CFS method with a BFS algorithm. Secondly, a data selection process applies a DRSA to generate the optimized dataset. Therefore, this study aims to solve the computational time complexity and increase the classification accuracy. Several datasets with various characteristics and volumes were used in the experimental process to evaluate the proposed method’s credibility. The method’s performance was validated using standard evaluation measures and benchmarked with other established methods such as deep learning (DL). Overall, the proposed work proved that it could assist the classifier in returning a significant result, with an accuracy rate of 82.1% for the neural network (NN) classifier, compared to the support vector machine (SVM), which returned 66.5% and 49.96% for DL. The one-way analysis of variance (ANOVA) statistical result indicates that the proposed method is an alternative extraction tool for those with difficulties acquiring expensive big data analysis tools and those who are new to the data analysis field.

• Publisher: MDPI AG
• Subject: Algorithms / Approximation / Big Data / big data; feature selection; correlation-based feature selection; deep learning; DRSA; neural network; support vector machines (SVM) / Classifiers / Computing time / correlation-based feature selection / Data acquisition / Data analysis / Data reduction / Datasets / Decision analysis / Decision making / deep learning / DRSA / Feature extraction / Feature selection / Fuzzy sets / JCR / Machine learning / Methods / neural network / Neural networks / Scopus / Set theory / Software / Support Vector Machine (SVM) / Support vector machines / support vector machines (SVM) / Variance analysis