Classifier ensemble methods in feature selection

Feature selection has become an indispensable preprocessing step in an expert system. Improving the feature selection performance could guide such a system to make better decisions. Classifier ensembles are known to improve performance when compared to the use of a single classifier. In this study, we aim to perform a formal comparison of different classifier ensemble methods on the feature selection domain. For this purpose, we compare the performances of six classifier ensemble methods: a greedy approach, two average-based approaches, two majority voting approaches, and a meta-classifier approach. In our study, the classifier ensemble involves five machine learning techniques: Logistic Regression, Support Vector Machines, Extreme Learning Machine, Naive Bayes, and Decision Tree. Experiments are carried on 12 well-known datasets, and results with statistical tests are provided. The results indicate that ensemble methods perform better than single classifiers, yet, they require a longer execution time. Moreover, they can minimize the number of features better than existing ensemble algorithms, namely Random Forest, AdaBoost, and Gradient Boosting, in a less amount of time. Among ensemble methods, the greedy based method performs well in terms of both classification accuracy and execution time. (c) 2020 Elsevier B.V. All rights reserved.

Automated summary

This study formally compares different classifier ensemble methods in the feature selection domain and finds that ensemble methods outperform single classifiers, albeit with longer execution time, and are more effective in minimizing the number of features.