Multi-view Ensemble Learning Using Optimal Feature Set Partitioning: An Extended Experiments and Analysis in Low Dimensional Scenario

Abstract

Abstract Multi-view ensemble learning (MEL) has successfully addressed the issue related to high dimensionality of the data. It exploits the information of views of the data. To obtain views of data, an optimal feature set partitioning (OFSP) method [1] has been shown performance enhancement of MEL. Results of the experiments carried out on datasets and their statistical analysis show the effectiveness for classification problem in high dimensional scenario. In this work, classification performance of MEL using OFSP method has been analyzed in low dimensional situations. Therefore, experiments are performed on low dimension datasets using K-Nearest Neighbor (KNN), Naive Bayesian (NB) and Support Vector Machine (SVM) classification algorithms. The experimental results and their statistical analysis show that OFSP method is also effective in low dimensional environment.