摘要
脑功能超网络的研究对脑疾病的准确诊断具有重要作用,目前已经有多种超网络的构建方法被应用于脑疾病的分类研究,但这些方法均未考虑到组间的重叠性问题。研究证明,组间的重叠性可能会对相关超网络模型的构建及构建后的分类应用产生影响,因此若仅使用非重叠组结构会限制其在超网络中的适用性。针对已应用于脑疾病分类研究的超网络构建方法在构建超网络模型时未考虑到分组之间的部分重叠性问题以及特征提取阶段的属性单一性问题,提出将多特征融合分析的重叠组套索方法应用于超网络的构建,并将其应用于抑郁症的诊断。结果表明,无论是在纯聚类系数属性下还是在多特征融合分析下,重叠组套索方法的分类性能较其他已有方法均有提高;在重叠组套索方法下,采用多特征融合分析较仅使用聚类系数属性分析获得了更高的分类准确率,达到了87.87%。
The study of brain function hypernetwork plays an important role in the accurate diagnosis of brain diseases.At pre-sent,there are a variety of hypernetwork construction methods used in the classification of brain diseases,but these methods do not take into account the overlap between groups.Studies have shown that the overlap between groups may affect the construction of related hypernetwork models and the classification application after construction.Therefore,if only non-overlapping group structures are used,it will limit its applicability in hypernetwork.Aiming at the hypernetwork construction method that has been applied to the study of brain disease classification,when constructing the hypernetwork model,the problem of partial overlap between groups and the problem of attribute singleness in the feature extraction stage are not considered,a method of overlapping group lasso with multi-feature fusion analysis is proposed.This research method is used in the construction of hypernetwork and applied to the diagnosis of depression.The results show that the classification performance of overlapping group lasso method is better than that of other existing methods in both pure clustering coefficient attribute and multi-feature fusion analysis.Under the overlapping group lasso method,the multi-feature fusion analysis achieves a higher classification accuracy than use the clustering coefficient attribute analysis alone,reaches 87.87%.
作者
李鹏祖
李瑶
Ibegbu Nnamdi JULIAN
孙超
郭浩
陈俊杰
LI Peng-zu;LI Yao;Ibegbu Nnamdi JULIAN;SUN Chao;GUO Hao;CHEN Jun-jie(College of Information and Computer,Taiyuan University of Technology,Taiyuan 030024,China)
出处
《计算机科学》
CSCD
北大核心
2022年第5期206-211,共6页
Computer Science
基金
国家自然科学基金(61672374,61876124)
山西省科技厅重点研发计划(201803D31043)。
关键词
抑郁症
功能超网络
重叠组套索
多特征融合
分类
Depression
Functional hypernetwork
Overlapping group lasso
Multi-feature fusion
Classification
