8 February 2017 Link prediction boosted psychiatry disorder classification for functional connectivity network
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Proceedings Volume 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016); 102252F (2017) https://doi.org/10.1117/12.2267698
Event: Eighth International Conference on Graphic and Image Processing, 2016, Tokyo, Japan
Abstract
Functional connectivity network (FCN) is an effective tool in psychiatry disorders classification, and represents cross-correlation of the regional blood oxygenation level dependent signal. However, FCN is often incomplete for suffering from missing and spurious edges. To accurate classify psychiatry disorders and health control with the incomplete FCN, we first ‘repair’ the FCN with link prediction, and then exact the clustering coefficients as features to build a weak classifier for every FCN. Finally, we apply a boosting algorithm to combine these weak classifiers for improving classification accuracy. Our method tested by three datasets of psychiatry disorder, including Alzheimer’s Disease, Schizophrenia and Attention Deficit Hyperactivity Disorder. The experimental results show our method not only significantly improves the classification accuracy, but also efficiently reconstructs the incomplete FCN.
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Weiwei Li, Weiwei Li, Xue Mei, Xue Mei, Hao Wang, Hao Wang, Yu Zhou, Yu Zhou, Jiashuang Huang, Jiashuang Huang, } "Link prediction boosted psychiatry disorder classification for functional connectivity network", Proc. SPIE 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016), 102252F (8 February 2017); doi: 10.1117/12.2267698; https://doi.org/10.1117/12.2267698
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