Ensemble multi-view feature set partitioning method for effective multi-view learning

Abstract

Multi-view learning consistently outperforms traditional single-view learning by leveraging multiple perspectives of data. However, the effectiveness of multi-view learning heavily relies on how the data are partitioned into feature sets. In many cases, different datasets may require different partitioning methods to capture their unique characteristics, making a single partitioning method insufficient. Finding an optimal feature set partitioning (FSP) for each dataset may be a time-consuming process, and the optimal FSP may still not be sufficient for all types of datasets. Therefore, the paper presents a novel approach called ensemble multi-view feature set partitioning (EMvFSP) to improve the performance of multi-view learning, a technique that uses multiple data sources to make predictions. The proposed EMvFSP method combines the different views produced by multiple partitioning methods to achieve better classification performance than any single partitioning method alone. The experiments were conducted on 15 structured datasets with varying ratios of samples, features, and labels, and the results showed that the proposed EMvFSP method effectively improved classification performance. The paper also includes statistical analyses using Friedman ranking and Holms procedure to demonstrate the effectiveness of the proposed method. This approach provides a robust solution for multi-view learning that can adapt to different types of datasets and partitioning methods, making it suitable for a wide range of applications.