Classification Approach by Mining Betweenness Information beyond Data Points Themselves
GU Suhang1,2,3, WANG Shitong1,2
1.Institute of Digital Media, Jiangnan University, Wuxi 214122 2.Jiangsu Key Laboratory of Media Design and Software Techno-logy, Jiangnan University, Wuxi 214122 3.Institute of Information Engineering and Technology, Changzhou Vocational Institute of Light Industry, Changzhou 213164
Abstract Mining useful information beyond data points themselves to guide and improve the accuracy of data classification is a subject worthy of study. In this paper, a network is firstly built to characterize the whole dataset, and the side information about the betweenness information between every pair of data points is mined in the network, and then the efficiency of subnetworks and the influence value of each network node are computed in an iterative way with the concept of density. With the mined inside information, a classification approach by mining betweenness information beyond data points themselves(CA-MBI) is developed. CA-MBI exhibits low time complexity with a high accuracy of data classification.The experimental results on synthetic and real-world datasets demonstrate the superior performance of CA-MBI compared with several benchmarking classification algorithms.
Corresponding Authors:
WANG Shitong, Ph.D., professor. His research interests include pattern recognition, artificial intelligence, machine learning and deep learning.
About author:: GU Suhang, Ph.D.candidate. His research interests include artificial intelligence, pattern recognition and machine learning.
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2018, Vol. 31 
