Team: MFS

Program: Applied Mathematics and Statistics, Biomedical Engineering, Computer Science

Univariate Independence Test based feature selection methods have been the standard for transforming datasets prior to classification. Wrapper based transformers that utilize classifier estimators are also highly effective at transforming datasets prior to classification with the same estimator. However, univariate based methods fail to capture multivariate dependencies, and wrapper methods are highly effective mainly when paired with the appropriate classifier; I propose a novel transformer that uses a user-specified Multivariate Independence Test as a wrapper object and provide specific examples of when this transformer can be optimally applied.