Computer Science ›› 2018, Vol. 45 ›› Issue (7): 293-298, 314.doi: 10.11896/j.issn.1002-137X.2018.07.050

• Interdiscipline & Frontier • Previous Articles     Next Articles

Multi-feature Fusion Classification Method Based on High-order Minimum Spanning Tree Brain Network

QIN Meng-na, CHEN Jun-jie, GUO Hao   

  1. College of Computer Science and Technology,Taiyuan University of Technology,Jinzhong,Shanxi 030600,China
  • Received:2017-06-02 Online:2018-07-30 Published:2018-07-30

Abstract: Existing researches on classification of brain diseases is based on the traditional low-order functional connectivity network.Low-order functional connectivity network may overlook the complex and dynamic interaction patterns among brain regions,which are essentially time-varying.The high-order functional connectivity network can reflect the abundant dynamic time information contained in the network.However,the traditional high-order functional connectivity network adopts the clustering method to reduce the dimensionality of the data,making the construced network can not be effectively interpreted from the perspective of neurology.Even more importantly,due to the large scale of the high-order functional connectivity network,it is verytime-comsuming to use some complex network or graph theory to calculate some topological properties.Therefore,this paper proposed a method for constructing a high-order minimum spanning tree network,calculated the traditional quantifiable network properties (degree and eccentricity),and used frequent subgraph mining technology to capture the discriminative subnetworks as features.Then,this paper applied a multi-kernel learning technique into the corresponding selected features to obtain the final classification results.The experimental results show that the classification accuracy is up to 97.54%.

Key words: Slow-order functional connectivity network, High-order functional connectivity network, Minimum spanning tree, Frequent subgraph

CLC Number: 

  • TP181
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