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

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.Ministry of Higher Education under the Fundamental Research Grant Scheme (FRGS/1/2018/ICT04/UTM/01/1)Universiti Teknologi Malaysia (UTM) under Research University Grant Vot-20H04, Malaysia Research University Network (MRUN) Vot 4L876SPEV project, University of Hradec Kralove, Faculty of Informatics and Management, Czech Republic (ID: 2102–2021), “Smart Solutions in Ubiquitous Computing Environments

Going Deeper than Supervised Discretisation in Processing of Stylometric Features

Rough set theory is employed in cases where data are incomplete and inconsistent and an ap- proximation of concepts is needed. The classical approach works for discrete data and allows only nominal classification. To induce the best rules, access to all available information is ad- vantageous, which can be endangered if discretisation is a necessary step in the data preparation stage. Discretisation, even executed with taking into account class labels of instances, brings some information loss. The research methodology illustrated in this paper is dedicated to ex- tended transformations of continuous input features into categorical, with the goal of enhancing the performance of rule-based classifiers, constructed with rough set data mining. The experi- ments were carried out in the stylometry domain, with its key task of authorship attribution. The obtained results indicate that supporting supervised discretisation with elements of unsuper- vised transformations can lead to enhanced predictions, which shows the merits of the proposed research framework