A review of feature set partitioning methods for multi-view ensemble learning

Abstract

Since the present era is entirely computer and Internet of Things (IoT) oriented, enormous amounts of data are produced quickly from many sources. Machine learning’s primary function, which is widely applicable to these high-dimensional datasets, is classification. One of the well-known approaches for improving classification effectiveness is ensemble learning. The splitting of huge datasets vertically based on the feature set partitioning (FSP) method is known as views. Multi-view ensemble learning (MEL) is the process of applying different classification models to these views and then combining their predictions. The FSP methods used to create views are the backbone of MEL. This review has been done to study, present, analyze, and collectively compare various FSP methods. Several issues and challenges related to FSP and MEL are also highlighted in this paper. The classification performance of recent FSP methods for the MEL framework is compared as an auxiliary experiment using fifteen high-dimensional datasets. A nonparametric statistical analysis is used for the comparison of FSP methods.