[1]张英,王骏,鲍国强,等.面向自闭症辅助诊断的无监督模糊特征学习新方法[J].智能系统学报,2019,14(05):882-888.[doi:10.11992/tis.201808005]
 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(05):882-888.[doi:10.11992/tis.201808005]
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面向自闭症辅助诊断的无监督模糊特征学习新方法(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第14卷
期数:
2019年05期
页码:
882-888
栏目:
出版日期:
2019-09-05

文章信息/Info

Title:
A novel unsupervised fuzzy feature learning method for computer-aided diagnosis of autism
作者:
张英 王骏 鲍国强 张春香 王士同
江南大学 数字媒体学院, 江苏 无锡 214122
Author(s):
ZHANG Ying WANG Jun BAO Guoqiang ZHANG Chunxiang WANG Shitong
School of Digital Media, Jiangnan University, Wuxi 214122, China
关键词:
自闭症功能磁共振成像功能连接皮尔森相关性特征选择无监督模糊特征映射流形正则化框架支持向量机
Keywords:
autismfunctional magnetic resonance imagingfunctional connectivityPearson’s correlationfeature selectionunsupervised fuzzy feature mappingmanifold regularization frameworksupport vector machine
分类号:
TP391
DOI:
10.11992/tis.201808005
摘要:
自闭症患者的行为和认知缺陷与潜在的脑功能异常有关。对于静息态功能磁振图像(functional magnetic resonance imaging, fMRI)高维特征,传统的线性特征提取方法不能充分提取其中的有效信息用于分类。为此,本文面向fMRI数据提出一种新型的无监督模糊特征映射方法,并将其与多视角支持向量机相结合,构建分类模型应用于自闭症的计算机辅助诊断。该方法首先采用多输出TSK模糊系统的规则前件学习方法,将原始特征数据映射到线性可分的高维空间;然后引入流形正则化学习框架,提出新型的无监督模糊特征学习方法,从而得到原输出特征向量的非线性低维嵌入表示;最后使用多视角SVM算法进行分类。实验结果表明:本文方法能够有效提取静息态fMRI数据中的重要特征,在保证模型具有优越且稳定的分类性能的前提下,还可以提高模型的可解释性。
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.

参考文献/References:

[1] DUAN Xujun, CHEN Heng, HE Changchun, et al. Resting-state functional under-connectivity within and between large-scale cortical networks across three low-frequency bands in adolescents with autism[J]. Progress in neuro-psychopharmacology and biological psychiatry, 2017, 79:434-441.
[2] DICKIE E W, AMEIS S H, SHAHAB S, et al. Personalized intrinsic network topography mapping and functional connectivity deficits in autism spectrum disorder[J]. Biological psychiatry, 2018, 84(4):278-286.
[3] 徐云, 杨健. 自闭症早期发现研究进展[J]. 中国临床心理学杂志, 2014, 22(6):1023-1027 XU Yun, YANG Jian. The research progress of the early recognization of autism[J]. Chinese journal of clinical psychology, 2014, 22(6):1023-1027
[4] BULLMORE E, SPORNS O. Complex brain networks:graph theoretical analysis of structural and functional systems[J]. Nature reviews neuroscience, 2009, 10(3):186-198.
[5] BULLMORE E, SPORNS O. The economy of brain network organization[J]. Nature reviews neuroscience, 2012, 13(5):336-349.
[6] PLIS S M, SUI Jing, LANE T, et al. High-order interactions observed in multi-task intrinsic networks are dominant indicators of aberrant brain function in schizophrenia[J]. NeuroImage, 2014, 102:35-48.
[7] CHEN Xiaobo, ZHANG Han, GAO Yue, et al. High-order resting-state functional connectivity network for MCI classification[J]. Human brain mapping, 2016, 37(9):3282-3296.
[8] MEUNIER D, ACHARD S, MORCOM A, et al. Age-related changes in modular organization of human brain functional networks[J]. NeuroImage, 2009, 44(3):715-723.
[9] DSOUZA A M, ABIDIN A Z, CHOCKANATHAN U, et al. Mutual connectivity analysis of resting-state functional MRI data with local models[J]. NeuroImage, 2018, 178:210-223.
[10] CORTES C, VAPNIK V. Support-vector networks[J]. Machine learning, 1995, 20(3):273-297.
[11] YAN Chaogan, ZANG Yufeng. DPARSF:a MATLAB toolbox for "pipeline" data analysis of resting-state fMRI[J]. Frontiers in systems neuroscience, 2010, 4(13):13.
[12] LAUFS H, DUNCAN J S. Electroencephalography/functional MRI in human epilepsy:what it currently can and cannot do[J]. Current opinion in neurology, 2007, 20(4):417-423.
[13] KESHAVARZI A, SARMADIAN F, SHIRI J, et al. Application of ANFIS-based subtractive clustering algorithm in soil Cation Exchange Capacity estimation using soil and remotely sensed data[J]. Measurement, 2017, 95:173-180.
[14] DAMAYANTI A. Fuzzy learning vector quantization, neural network and fuzzy systems for classification fundus eye images with wavelet transformation[C]//Proceedings of the 2nd International Conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE). Wuhan, China, 2017:331?336.
[15] DENG Zhaohong, JIANG Yizhang, CHOI K S, et al. Knowledge-leverage-based TSK Fuzzy System modeling[J]. IEEE transactions on neural networks and learning systems, 2013, 24(8):1200-1212.
[16] ZHANG Daoqiang, WANG Yaping, ZHOU Luping, et al. Multimodal classification of Alzheimer’s disease and mild cognitive impairment[J]. NeuroImage, 2011, 55(3):856-867.
[17] POWERS D M W. Evaluation:from precision, recall and F-factor to ROC, informedness, markedness and correlation[J]. Journal of machine learning technologies, 2011, 2(1):37-83.
[18] CHANG C C, LIN C J. LIBSVM:a library for support vector machines[J]. ACM transactions on intelligent systems and technology, 2011, 2(3):27.

备注/Memo

备注/Memo:
收稿日期:2018-08-08。
基金项目:江苏省自然科学基金项目(BK20181339).
作者简介:张英,女,1992年生,硕士研究生,主要研究方向为模式识别与数据挖掘;王骏,男,1978年生,副教授,博士,主要研究方向为智能计算与数据挖掘。主持国家自然科学基金项目1项,江苏省自然科学基金项目1项,申请并获得国家发明专利5项。于2016年获江苏省高校科研成果自然科学一等奖。发表学术论文50余篇;鲍国强,男,1992年生,硕士研究生,主要研究方向为智能计算与模式识别。
通讯作者:王骏.E-mail:wangjun_sytu@hotmail.com
更新日期/Last Update: 1900-01-01