Improving multi-view ensemble learning with Round-Robin feature set partitioning

Abstract

Multi-view Ensemble Learning (MEL) techniques have shown remarkable success in improving the accuracy and resilience of classification algorithms by combining multiple base classifiers trained over different perspectives of a dataset, known as views. One crucial factor affecting ensemble performance is the selection of diverse and informative feature subsets. Feature Set Partitioning (FSP) methods address this challenge by creating distinct views of features for each base classifier. In this context, we propose the Round-Robin Feature Set Partitioning ($\mathrm{RR}$-FSP) technique, which introduces a novel approach to feature allocation among views. This novel approach evenly distributes highly correlated features across views, thereby enhancing ensemble diversity, promoting balanced feature utilization, and encouraging the more equitable distribution of correlated features, $\mathrm{RR}$-FSP contributes to the advancement of MEL techniques. Through experiments on various datasets, we demonstrate that $\mathrm{RR}$-FSP offers improved classification accuracy and robustness, making it a valuable addition to the arsenal of FSP techniques for MEL.