Multi-Objective Genetic Algorithm for Multi-View Feature Selection

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

Comparison of single and multi-task learning for predicting cognitive decline based on MRI data

Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-Cog) is a neuropsychological tool that has been designed to assess the severity of cognitive symptoms of dementia. Personalized prediction of the changes in ADAS-Cog scores could help in the timing of therapeutic interventions in dementia and at-risk populations. In the present work, we compared single- and multi-task learning approaches to predict the changes in ADAS-Cog scores based on T1-weighted anatomical magnetic resonance imaging (MRI). In contrast to most machine learning-based methods to predict the changes in ADAS-Cog, we stratified the subjects based on their baseline diagnoses and evaluated the prediction performances in each group. Our experiments indicated a positive relationship between predicted and observed ADAS-Cog score changes in each diagnostic group suggesting that T1-weighted MRI has predictive value for evaluating cognitive decline in the whole AD continuum. We further studied whether correction of the differences in magnetic field strength of MRI would improve ADAS-Cog score prediction. The partial least square-based domain adaptation improved slightly prediction performance, but the improvement was marginal. In sum, this study demonstrated that ADAS-Cog change can be, to some extent, predicted based on anatomical MRI. Based on this study, the recommended method for learning the predictive models is a single-task regularized linear regression owing to its simplicity and good performance. It appears important to combine the training data across all subject groups for the most effective predictive models.publishedVersionPeer reviewe