Application of Chi-square discretization algorithms to ensemble classification methods

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. © 2021 Elsevier Ltd