[1]ZHANG Ying,WANG Jun,BAO Guoqiang,et al.A novel unsupervised fuzzy feature learning method for computer-aided diagnosis of autism[J].CAAI Transactions on Intelligent Systems,2019,14(5):882-888.[doi:10.11992/tis.201808005]
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A novel unsupervised fuzzy feature learning method for computer-aided diagnosis of autism
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
14
Number of periods:
2019 5
Page number:
882-888
Column:
学术论文—机器学习
Public date:
2019-09-05
- Title:
- A novel unsupervised fuzzy feature learning method for computer-aided diagnosis of autism
- Keywords:
- autism; functional magnetic resonance imaging; functional connectivity; Pearson’s correlation; feature selection; unsupervised fuzzy feature mapping; manifold regularization framework; support vector machine
- CLC:
- TP391
- DOI:
- 10.11992/tis.201808005
- Abstract:
- Studies have shown that the behavioral and cognitive defect of patients with autism have a close relationship with potential brain dysfunction. For the high-dimensional rs-fMRI features, traditional linear feature extraction method cannot always discriminatively extract the important information for classification. To this end, a novel method for fMRI data based on both unsupervised fuzzy feature mapping and multi-view support vector machine is proposed in this study, which aims to build a classification model for computer aided diagnosis of autism. In this method, the original features are first mapped to a linear separable high-dimensional space using the rule precursor learning method of multi-output Takagi-Sugeno-Kang (TSK) fuzzy system; then the manifold regularization learning framework is introduced. On the basis of this, a novel unsupervised fuzzy feature learning method is used to obtain the nonlinear low-dimensional embedding representation of the original output eigenvector. Finally, a multi-view support vector machine (SVM) algorithm is used for classification. The experimental results show that the proposed method can effectively extract important features from the rs-fMRI data and improve the interpretability of the model on the premise of ensuring a superior and stable classification performance of the model.
- References:
-
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