【摘要】：最近,腦連接網絡已經被用于神經退行性疾病(如阿爾茨海默病AD以及輕度認知障礙MCI)的診斷和分類.以往典型方法是從腦連接網絡中提取一些特征(如局部聚類系數等)構成一個長特征向量,并用其訓練一個分類器用于最終的分類.然而,上述方法的一個缺點是未能充分考慮網絡的拓撲結構信息,因而限制了分類性能的進一步提升.提出一種基于判別子圖挖掘的腦連接網絡分類方法.首先分別從正類訓練樣本集和負類訓練樣本集中挖掘頻繁子網絡(即頻繁子圖);然后利用基于圖核的方法來衡量頻繁子網絡的判別性能,并選擇那些最具判別性的頻繁子網絡作為判別子網絡用于后續(xù)的分類;最后,在真實MCI數據集上的實驗驗證了該方法的有效性.
[Abstract]:Recently, brain connectivity networks have been used in the diagnosis and classification of neurodegenerative diseases such as Alzheimer's disease (AD) and mild cognitive impairment (MCI). In the past, some features (such as local clustering coefficients) were extracted from the brain junction network to form a long feature vector, and a classifier was used to train a classifier for the final classification. However, one of the disadvantages of the above methods is that the topology information of the network is not fully considered, which limits the further improvement of the classification performance. In this paper, a classification method of brain connection network based on discriminant subgraph mining is proposed. Firstly, frequent subnetworks (i.e. frequent subgraphs) are mined from positive and negative training samples respectively, and then the discriminant performance of frequent subnetworks is evaluated by using graph kernel-based methods. The most discriminant frequent subnetworks are selected as discriminant subnetworks for subsequent classification. Finally, the effectiveness of the proposed method is verified by experiments on real MCI datasets.
【作者單位】： 南京航空航天大學計算機科學與技術學院;
【基金】：江蘇省自然科學基金杰出青年基金(BK20130034) 高等學校博士學科點專項基金(20123218110009) 南京航空航天大學基本科研業(yè)務費(NE2013105) 中央高校基本科研業(yè)務專項資金(NZ2013306)
【分類號】：TP311.13;R749.1

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