Multi-Objective Genetic Algorithm for Multi-View Feature Selection

TL;DR

This paper introduces a novel multi-view multi-objective genetic algorithm (MMFS-GA) for feature selection that improves prediction accuracy and interpretability in multi-view datasets by effectively selecting relevant features across multiple data modalities.

Contribution

The paper presents a new genetic algorithm approach that simultaneously selects features within and across views, addressing limitations of traditional methods and enhancing multi-view data analysis.

Findings

Superior performance on benchmark datasets

Improved interpretability of selected features

Effective handling of high-dimensional multi-view data

Abstract

Multi-view datasets offer diverse forms of data that can enhance prediction models by providing complementary information. However, the use of multi-view data leads to an increase in high-dimensional data, which poses significant challenges for the prediction models that can lead to poor generalization. Therefore, relevant feature selection from multi-view datasets is important as it not only addresses the poor generalization but also enhances the interpretability of the models. Despite the success of traditional feature selection methods, they have limitations in leveraging intrinsic information across modalities, lacking generalizability, and being tailored to specific classification tasks. We propose a novel genetic algorithm strategy to overcome these limitations of traditional feature selection methods for multi-view data. Our proposed approach, called the multi-view multi-objective feature selection genetic algorithm (MMFS-GA), simultaneously selects the optimal subset of features within a view and between views under a unified framework. The MMFS-GA framework demonstrates superior performance and interpretability for feature selection on multi-view datasets in both binary and multiclass classification tasks. The results of our evaluations on three benchmark datasets, including synthetic and real data, show improvement over the best baseline methods. This work provides a promising solution for multi-view feature selection and opens up new possibilities for further research in multi-view datasets.