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

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


Abstract:

Hypernetworks are a weighted hypergraph where evolutionary methods are learning the model structure and parameters. The evolutionary methods enable the hypernetwork model...Show More

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.
Date of Conference: 20-24 August 2009
Date Added to IEEE Xplore: 02 October 2009
ISBN Information:
Print ISSN: 1098-7584
Conference Location: Jeju, Korea (South)

References

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