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Hippocampal shape classification using redundancy constrained feature selection

Journal Article


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Abstract


  • Landmark-based 3D hippocampal shape classification involves high-dimensional descriptor space, many noisy and redundant features, and a very small number of training samples. Feature selection becomes critical in this situation, because it not only improves classification performance, but also identifies the regions that contribute more to shape discrimination. This work identifies the drawbacks of SVM-RFE, and proposes a novel class-separability-based feature selection approach to overcome them. We formulate feature selection as a constrained integer optimization and develop a new algorithm to efficiently and optimally solve this problem. Theoretical analysis and experimental study on both synthetic data and real hippocampus data demonstrate its superior performance over the prevailing SVM-RFE. Our work provides a new efficient feature selection tool for hippocampal shape classification.

Authors


Publication Date


  • 2010

Citation


  • Zhou, L., Wang, L., Shen, C. & Barnes, N. (2010). Hippocampal shape classification using redundancy constrained feature selection. Lecture Notes in Computer Science, 6362 266-273. Beijing Hippocampal shape classification using redundancy constrained feature selection

Scopus Eid


  • 2-s2.0-78349250170

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1610&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/604

Has Global Citation Frequency


Number Of Pages


  • 7

Start Page


  • 266

End Page


  • 273

Volume


  • 6362

Place Of Publication


  • Germany

Abstract


  • Landmark-based 3D hippocampal shape classification involves high-dimensional descriptor space, many noisy and redundant features, and a very small number of training samples. Feature selection becomes critical in this situation, because it not only improves classification performance, but also identifies the regions that contribute more to shape discrimination. This work identifies the drawbacks of SVM-RFE, and proposes a novel class-separability-based feature selection approach to overcome them. We formulate feature selection as a constrained integer optimization and develop a new algorithm to efficiently and optimally solve this problem. Theoretical analysis and experimental study on both synthetic data and real hippocampus data demonstrate its superior performance over the prevailing SVM-RFE. Our work provides a new efficient feature selection tool for hippocampal shape classification.

Authors


Publication Date


  • 2010

Citation


  • Zhou, L., Wang, L., Shen, C. & Barnes, N. (2010). Hippocampal shape classification using redundancy constrained feature selection. Lecture Notes in Computer Science, 6362 266-273. Beijing Hippocampal shape classification using redundancy constrained feature selection

Scopus Eid


  • 2-s2.0-78349250170

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1610&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/604

Has Global Citation Frequency


Number Of Pages


  • 7

Start Page


  • 266

End Page


  • 273

Volume


  • 6362

Place Of Publication


  • Germany