Application of Chi-square discretization algorithms to ensemble classification methods

• Published in: Expert systems with applications Vol. 185; p. 115540
• Main Authors: Peker, Nuran, Kubat, Cemalettin
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 15.12.2021; Elsevier BV
• Subjects: Algorithms; Chi-square statistics; Chi-square test; Classification; Continuity (mathematics); Data mining; Datasets; Decision trees; Discretization; Ensemble methods; Machine learning; Ensemble methods; Discretization; Data mining; Machine learning; Classification; Chi-square statistics
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2021.115540

•Discretization is an important process in terms of both machine learning and data mining.•The effect of discretization on ensemble methods was analyzed.•Four Chi-square based discretization algorithms were used.•The Ensemble methods performed better on discrete data sets. Classification is one of the important tasks in data mining and machine learning. Classification performance depends on many factors as well as data characteristics. Some algorithms are known to work better with discrete data. In contrast, most real-world data contain continuous variables. For algorithms working with discrete data, these continuous variables must be converted to discrete ones. In this process called discretization, continuous variables are converted to their corresponding discrete variables. In this paper, four Chi-square based supervised discretization algorithms ChiMerge(ChiM), Chi2, Extended Chi2(ExtChi2) and Modified Chi2(ModChi2) were used. In the literature, the performance of these algorithms is often tested with decision trees and Naïve Bayes classifiers. In this study, differently, four sets of data discretized by these algorithms were classified with ensemble methods. Classification accuracies for these data sets were obtained through using a stratified 10-fold cross-validation method. The classification performance of the original and discrete data sets of the methods is presented comparatively. According to the results, the performance of the discrete data is more successful than the original data.