Minimum spanning tree clustering approach for effective feature partitioning in multi-view ensemble learning

Abstract

This paper introduces a novel approach for feature set partitioning in multi-view ensemble learning (MVEL) utilizing the minimum spanning tree clustering (MSTC) algorithm. The proposed method aims to generate informative and diverse feature subsets to enhance classification performance in the MVEL framework. The MSTC algorithm constructs a minimum spanning tree based on correlation measures and divides features into non-overlapping clusters, representing distinct views used to improve ensemble learning. We evaluate the effectiveness of the MSTC-based MVEL framework on ten high-dimensional datasets using support vector machines. Results indicate significant improvements in classification performance compared to single-view learning and other cutting-edge feature partitioning approaches. Statistical analysis confirms the enhanced classification accuracy achieved by the proposed MVEL framework, reaching a level of accuracy that is both reliable and competitive.