ZHOU Jingjing, SUN Weifeng, HAN Xiaomin, LU Ruqiang, ZHANG Yuanqi, ZHANG Shenwei. The Research of One Novel Cost-Sensitive Classification Algorithm[J]. Chinese Journal of Electronics, 2018, 27(5): 1015-1024. doi: 10.1049/cje.2018.01.002
Citation: ZHOU Jingjing, SUN Weifeng, HAN Xiaomin, LU Ruqiang, ZHANG Yuanqi, ZHANG Shenwei. The Research of One Novel Cost-Sensitive Classification Algorithm[J]. Chinese Journal of Electronics, 2018, 27(5): 1015-1024. doi: 10.1049/cje.2018.01.002

The Research of One Novel Cost-Sensitive Classification Algorithm

doi: 10.1049/cje.2018.01.002
Funds:  This work is supported by the National Social Science Fund of China (No.16BTQ086).
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  • Corresponding author: SUN Weifeng (corresponding author) was born in 1978, associate professor, major in computer science. He received the B.E. degree from University of Science and Technology of China in 2002. He received the Ph.D. degree from University of Science and Technology of China in 2007. His research interests include computer network and network protocol. (Email:wfsun@dlut.edu.cn)
  • Received Date: 2017-01-06
  • Rev Recd Date: 2017-03-17
  • Publish Date: 2018-09-10
  • Assuming that misclassification costs between different categories are equal, traditional Graph based semi-supervised classification (GSSC) algorithms pursues high classification accuracy. In many practical problems, especially in the fields of finance and medicine, compared with global classification accuracy, less cost on global misclassification is more likely to be the most significant factor. We propose one novel cost-sensitive classification algorithm based on the local and global consistency, which utilizes the semi-supervised classification algorithms better, and ensures higher classification accuracy on the basis of reducing overall cost. Our improved algorithm may bring some problems due to unbalanced data account, so we introduce synthetic minority oversampling technique algorithm for further optimization. Experimental results of bank loans and medical problems verify the effectiveness of our novel classification algorithm.
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