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Proceedings Paper

Link prediction boosted psychiatry disorder classification for functional connectivity network
Author(s): Weiwei Li; Xue Mei; Hao Wang; Yu Zhou; Jiashuang Huang
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Paper 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.

Paper Details

Date Published: 8 February 2017
PDF: 9 pages
Proc. SPIE 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016), 102252F (8 February 2017); doi: 10.1117/12.2267698
Show Author Affiliations
Weiwei Li, Nanjing Tech Univ. (China)
Xue Mei, Nanjing Tech Univ. (China)
Hao Wang, Nanjing Tech Univ. (China)
Yu Zhou, Nanjing Tech Univ. (China)
Jiashuang Huang, Nanjing Univ. of Aeronautics and Astronautics (China)


Published in SPIE Proceedings Vol. 10225:
Eighth International Conference on Graphic and Image Processing (ICGIP 2016)
Yulin Wang; Tuan D. Pham; Vit Vozenilek; David Zhang; Yi Xie, Editor(s)

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