Multi-Objective Genetic Algorithm for Multi-View Feature Selection

TL;DR

The 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 that demonstrates superior performance and interpretability for feature selection on multi-View datasets in both binary and multiclass classification tasks.

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.

Figures (8)

• Figure 1: An illustrative example of a multimodal multi-objective problem.
• Figure 2: The overall framework for multi-view Feature Selection using Genetic Algorithm (MMFS-GA).
• Figure 3: The parallel framework for the multiniche Multi-view Multi-objective Feature Selection Genetic Algorithm (MMFS-GA).
• Figure 4: Comparison of the MMFS-GA algorithm against the best baseline methods for binary classification. The F1 scores between informative and selected features are shown at the top of each bar. Blue bars represent the number of selected features from the view with high discriminative power; pink bars represent the number of selected features from the view with low discriminative power; and cyan, violet, green indicate selections from noise groups.
• Figure 5: ROC curves of all classification methods for AD versus NC and MCI versus NC. Compared to existing approaches, the proposed method obtains the best performance with the highest AUC, a high true positive rate (TPR), and a low false positive rate (FPR).