Gender classification with cortical thickness measurement from magnetic resonance imaging by using a feature selection method based on evolutionary hypernetworks

Authors:
Jung-Woo Ha;Joon Hwan Jang;Do-Hyung Kang;Wi Hoon Jung;Jun Soo Kwon;Byoung-Tak Zhang
Affiliations:
Biointelligence Lab, School of Computer Science and Engineering, Seoul National University, Seoul, Korea;Department of Psychiatry, College of Medicine, Seoul National University, South Korea;Department of Psychiatry, College of Medicine, Seoul National University, South Korea;Seoul National University, Seoul, Korea;Department of Psychiatry, College of Medicine, Seoul National University, South Korea;Biointelligence Lab, School of Computer Science and Engineering, Seoul National University, Seoul, Korea
Venue:
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Year:
2009

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Abstract

Hypernetworks are a weighted hypergraph where evolutionary methods are learning the model structure and parameters. The evolutionary methods enable the hypernetwork model to conserve significant features implicitly during the learning process. In this study, we propose a novel feature selection method based on occurrence frequencies of attributes in hyperedges by analyzing the structure of a hypernetwork. We also apply the evolutionary hypernetwork with the proposed feature selection method to the gender classification based on cortical thickness measurement on healthy young adults from Magnetic Resonance Imaging (MRI). The experimental results show that the proposed selection method improves the classification accuracy by approximately 20%. Also, a comparative study on four classification algorithms and three feature selection methods shows that the hypernetwork model with the proposed feature selection method achieves a competitive classification performance.