A Novel Approach of Ensemble Methods Using the Stacked Generalization for High-dimensional Datasets

AbstractStacked generalization-based heterogeneous ensemble methods combine the prediction of multiple classifiers to improve overall classification performance. Several stacking methods are available in the literature, but the criteria to select the number and type of classifiers are missing. This work analyzes the performance of stacked generalization-based ensemble machine learning methods for high-dimensional datasets. Also, the impact of the classifier selection for the first level (L0) of the stacked generalization method has been studied. Based on that, the criteria for selecting classifiers at the first level of the stacked generalization method are proposed. So, six stacked generalization approaches are presented and have been analyzed for the thirty high-dimensional datasets. The experiments and results indicate that the performance of stacking strategies based on proposed selection criteria performs better. Also, a comparative study about the choice of homogeneous ensemble classifiers in stacked generalization concerning the use of basic classifiers and fusion of basic with homogeneous ensemble methods has been made. It has been observed that the use of only homogeneous ensemble classifiers is not beneficial in the stacked generalization methods. Also, the performance of stacked generalization based on basic classifiers or a combination of homogeneous ensemble methods with basic classifiers is better than homogeneous ensemble methods. The proposed stacking approach based on a combination of the basic and ensemble classifiers has improved the accuracy 0.72% to 8.46%. The impact of removing redundant and non-relevant features on the proposed stacking approaches has been evaluated.KEYWORDS: Ensemble classifierfeature selectionhigh-dimensional datastacked generalization Disclosure statementNo potential conflict of interest was reported by the author(s).DATA AVAILABILITY STATEMENTThe data that support the findings of this study are available in the ASU feature selection repository [https://jundongl.github.io/scikit-feature/datasets.html],OpenML [https://www.openml.org], and GitHub [https://github.com/ramhiser/datamicroarray].Additional informationNotes on contributorsSuvita Rani SharmaSuvita Rani Sharma received the Master of Technology degree in Computer Science and Engineering from Sant Longowal Institute of Engineering and Technology, Longowal, India. Now, she is pursuing a Ph.D. in Computer Science and Engineering from the same institute. Her research interests include machine learning, feature selection, and metaheuristic optimization techniques. Email: suvita.sharma1204@gmail.comBirmohan SinghBirmohan Singh is working as a professor in the Department of Computer Science and Engineering, Sant Longowal Institute of Engineering and Technology, Longowal. His research interests include signal processing, image processing, machine learning, and metaheuristic optimization techniques. Corresponding author. Email: birmohans@gmail.comManpreet KaurManpreet Kaur is a professor in the Department of Electrical and Instrumentation Engineering, Sant Longowal Institute of Engineering and Technology, Longowal, India. Her research interests include biomedical signal processing, image processing, and machine learning. Email: aneja_mpk@yahoo.com