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