A Framework for Feature Selection to Exploit Feature Group Structures

Perera, Kushani; Chan, Jeffrey; Karunasekera, Shanika

Filter feature selection methods play an important role in machine learning tasks when low computational costs, classifier independence or simplicity is important. Existing filter methods predominantly focus only on the input data and do not take advantage of the external sources of correlations within feature groups to improve the classification accuracy. We propose a framework which facilitates supervised filter feature selection methods to exploit feature group information from external sources of knowledge and use this framework to incorporate feature group information into minimum Redundancy Maximum Relevance (mRMR) algorithm, resulting in GroupMRMR algorithm. We show that GroupMRMR achieves high accuracy gains over mRMR (up to [Formula: see text]35%) and other popular filter methods (up to [Formula: see text]50%). GroupMRMR has same computational complexity as that of mRMR, therefore, does not incur additional computational costs. Proposed method has many real world applications, particularly the ones that use genomic, text and image data whose features demonstrate strong group structures.