Multi-view ensemble learning using multi-objective particle swarm optimization for high dimensional data classification
Vipin Kumar, Prem Shankar Singh Aydav, Sonajharia Minz
Abstract
In state-of-the-art, it has proven that multi-view ensemble learning performs better than classical machine learning algorithms, with the optimized setting of views (subsets of features) usually. Obtaining the appropriate number of views for a given dataset is a complex problem in multi-view ensemble learning (MEL). The finding of the total number of possible views is an NP-hard problem, i.e., equivalent to Bell number. Moreover, the complexity of multi-view learning increases over a higher number of views of the dataset. Therefore, it is highly required to consider a smaller number of views with higher accuracy for optimal performance of MEL. In this work, MEL using Multi-Objective Particle Swarm Optimization (MEL-MOPSO) method has been proposed. The two objectives (number of views of the data and classification accuracy of MEL) have considered where the trade-off between objectives has been performed while searching for an optimal solution using Particle Swarm Optimization (PSO) in the process of multiobjective optimization. The experiments have been done over sixteen high-dimensional datasets using four state-of-art view construction methods. The individual views of the dataset has been utilized to learn through a support vector machine algorithm. The quantitative and non-parametric statistical analyses show that the proposed method has performed effectively and efficiently.