An Optimal Multi-view Ensemble Learning for High Dimensional Data Classification Using Constrained Particle Swarm Optimization

Abstract

Multiple views of a dataset are constructed using the Feature Set Partitioning (FSP) methods for Multi-view Ensemble Learning (MEL). The way of partitioning of features influences the classification performance of MEL. The possible numbers of features set partition of the dataset are equal to the Bell number, which is in polynomial nature and a NP-hard problem (Shown in Fig. 1). It is essential to find an optimal classification performance of MEL for a features set partition among all possible features set partition in high dimension scenario. Therefore, an optimal multi-view ensemble learning approach using constrained particle swarm optimization method (OMEL-C-PSO) is proposed for high dimensional data classification. The experiments have been performed on ten high dimensional datasets. Using four exiting features set partitioning methods; the result shows that OMEL-C-PSO approach is feasible and effective for high dimensional data classification.