基于鱼群算法的脑功能连接邻域粗糙集特征归约方法

doi:10.3785/j.issn.1008-973X.2020.11.020

浙江大学学报(工学版)

2020, Vol. 54

Issue (11): 2247-2257 DOI: 10.3785/j.issn.1008-973X.2020.11.020

生物医学工程

基于鱼群算法的脑功能连接邻域粗糙集特征归约方法

冀俊忠1,2(

),宋晓妮1,2,杨翠翠1,2,*(

)

1. 北京工业大学信息学部，多媒体与智能软件技术北京市重点实验室，北京 100124
2. 北京工业大学北京人工智能研究院，北京 100124

Feature reduction of neighborhood rough set based on fish swarm algorithm in brain functional connectivity

Jun-zhong JI1,2(

),Xiao-ni SONG1,2,Cui-cui YANG1,2,*(

)

1. Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
2. Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing 100124, China

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摘要：

为了有效应对脑功能连接高维小样本性给分类模型构建带来的挑战，得到与脑疾病诊断相关的重要特征，提出基于鱼群算法的脑功能连接邻域粗糙集特征归约方法.该方法建立脑功能连接数据的邻域决策表；依据特征的依赖度将鱼个体初始化为候选的脑功能连接特征子集，并采用综合特征子集依赖度和特征子集长度的适应度函数对鱼个体进行评价；在种群优化过程中，执行觅食、聚集、追尾机制，以及交叉和迁徙2个新机制来不断搜索最优的特征子集.在3种脑疾病功能磁共振脑成像（fMRI）数据集上，将所提方法与多种已有的特征归约方法进行对比实验.结果表明，该方法是有效的脑功能连接特征归约方法，可以有效降低脑功能连接数据的维度，获得分类判别能力较强的脑功能连接特征.

关键词： 脑功能连接; 特征归约; 鱼群算法; 邻域粗糙集

Abstract:

Feature reduction of neighborhood rough set based on fish swarm algorithm in brain functional connectivity was proposed, in order to effectively deal with the challenge brought by the high-dimensional and small sample size of brain functional connectivity to the construction of classification model, and to obtain important features related to brain disease diagnosis. The neighborhood decision table of the brain functional connectivity is established in the algorithm. Each artificial fish is initialized as a candidate feature subset of the brain functional connectivity according to the feature dependence information, and a fitness function is constructed based on the information of feature subset dependence and feature subset length to evaluate each individual. The preying, swarming, following mechanisms of the fish swarm algorithm, as well as two new simulation mechanisms of crossover and migration are performed to iteratively search for the optimal feature subset in the process of population optimization. The proposed method was compared with a variety of existing feature reduction methods in the functional magnetic resonance imaging (fMRI) data sets of three brain diseases. Results show that the new method is an effective feature reduction method for the brain functional connectivity, which can effectively reduce the dimension of the brain functional connectivity data and obtain the brain functional connectivity features with high classification discrimination ability.

Key words: brain functional connectivity feature reduction fish swarm algorithm neighborhood rough set

收稿日期: 2019-11-11 出版日期: 2020-12-15

CLC:

TP 301

基金资助: 国家自然科学基金资助项目(61672065，61906010)；北京市教委科技资助项目(KM202010005032)；中国博士后科学基金资助项目(2018M631291)；北京市博士后工作经费资助项目(2017-ZZ-024)；朝阳区博士后工作经费资助项目(2018ZZ-01-05)

通讯作者: 杨翠翠 E-mail: jjz01@bjut.edu.cn;yangcc@bjut.edu.cn

作者简介: 冀俊忠(1969—)，男，教授，博士，从事生物信息学研究. orcid.org/0000-0001-6951-741X. E-mail： jjz01@bjut.edu.cn

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引用本文:

冀俊忠,宋晓妮,杨翠翠. 基于鱼群算法的脑功能连接邻域粗糙集特征归约方法[J]. 浙江大学学报(工学版), 2020, 54(11): 2247-2257.

Jun-zhong JI,Xiao-ni SONG,Cui-cui YANG. Feature reduction of neighborhood rough set based on fish swarm algorithm in brain functional connectivity. Journal of ZheJiang University (Engineering Science), 2020, 54(11): 2247-2257.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2020.11.020 或 http://www.zjujournals.com/eng/CN/Y2020/V54/I11/2247

表 1 静息态fMRI数据集

图 1 NRSFSA算法在不同测试参数下的评价指标

图 2 NRSFSA算法在不同邻域半径下的实验结果

图 3 NRSFSA算法在数据集上的迭代过程

表 2 8种算法在5组实验数据上的实验结果

表 2

表 3 8种算法在数据集上运行时间的比较

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