摘要
目的与单标签分类相比,多标签分类在现实世界中更为常见。在神经影像学领域,对个体认知单标签分类的研究有很多,但是却没有关于个体认知多标签分类的研究。本研究尝试运用机器学习多标签分类算法,利用静息态fMRI数据,对个体认知进行多标签分类研究。方法基于390名(≥18岁)成年人的静息态fMRI数据,通过5种具有代表性的机器学习多标签分类算法:ML-kNN、hMuLab、LIFT、ML-LOC和GLOCAL对个人认知进行多标签分类,采用十折交叉验证进行训练和测试,并用多标签分类常用的评价指标进行结果的检验。结果这5种算法都能用于静息态fMRI的多标签分类研究,且经过算法对比发现hMuLab算法的分类效果最好。结论本研究成功地将多标签分类算法应用于神经影像学领域并对个体认知进行多标签分类,且通过算法对比发现样本的邻域信息对分类结果很重要。
Objective Compared with single label classification,multi-label classification is more common in the real world.In the field of neuroimaging,there are many studies on single label classification of individual cognition,and no research on multi-label classification of individual cognition.We try to use machine learning multi-label classification algorithm and resting state fMRI data to study the multi-label classification of individual cognition.Methods Based on the resting state fMRI data of 390 adults(≥18 years old),five representative algorithms of machine learning multi-label classification:ML-kNN,hMuLab,LIFT,ML-LOC and GLOCAL were used to classify individual cognition,ten fold cross validation was used for training and testing,and commonly used evaluation criteria for multi-label classification were used to test the results.Results The results of each evaluation criterion showed that these five algorithms could be used in the multi-label classification of resting state fMRI,and the hMuLab algorithm had the best classification effect compared with other algorithms.Conclusions We successfully applied the multi-label classification algorithm to multi-label classification of individual cognition in the field of neuroimaging,and found that the sample neighborhood information was important by comparing the five algorithms.
作者
吴怊慧
WU Chaohui(School of Computer and Information Technology,Beijing Jiaotong University,Beijing 100044)
出处
《北京生物医学工程》
2020年第2期138-144,178,共8页
Beijing Biomedical Engineering
关键词
静息态功能性磁共振成像
个体认知
机器学习
多标签分类
算法对比
resting state functional magnetic resonance imaging
individual cognition
machine learning
multi-label classification
algorithm comparison