Random Forest for Dissimilarity-based Multi-view Learning

TL;DR

This paper introduces a novel multi-view learning approach using Random Forest dissimilarity measures and a Dynamic View Selection method to improve classification performance on multi-view datasets.

Contribution

It proposes using Random Forest proximity for dissimilarity representation and a dynamic method to select relevant views per instance, enhancing multi-view classification.

Findings

Dynamic View Selection significantly outperforms static methods.

Random Forest dissimilarity effectively captures class and feature similarities.

Method improves accuracy on real-world multi-view datasets.

Abstract

Many classification problems are naturally multi-view in the sense their data are described through multiple heterogeneous descriptions. For such tasks, dissimilarity strategies are effective ways to make the different descriptions comparable and to easily merge them, by (i) building intermediate dissimilarity representations for each view and (ii) fusing these representations by averaging the dissimilarities over the views. In this work, we show that the Random Forest proximity measure can be used to build the dissimilarity representations, since this measure reflects similarities between features but also class membership. We then propose a Dynamic View Selection method to better combine the view-specific dissimilarity representations. This allows to take a decision, on each instance to predict, with only the most relevant views for that instance. Experiments are conducted on several real-world multi-view datasets, and show that the Dynamic View Selection offers a significant improvement in performance compared to the simple average combination and two state-of-the-art static view combinations.