[1]PAN Zhuqiang,ZHANG Lin,ZHANG Lei,et al.Research on classification of diseases of clinical imbalanced data in traditional Chinese medicine[J].CAAI Transactions on Intelligent Systems,2017,12(6):848-856.[doi:10.11992/tis.201706046]
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Research on classification of diseases of clinical imbalanced data in traditional Chinese medicine

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 12 Number of periods: 2017 6 Page number: 848-856 Column: 学术论文—智能系统 Public date: 2017-12-25
Title:
Research on classification of diseases of clinical imbalanced data in traditional Chinese medicine
Author(s):
PAN Zhuqiang1;  ZHANG Lin1;  ZHANG Lei2;  LI Guozheng3;  YAN Shixing4
1. School of Computer Science, Southwest Petroleum University, Chengdu 610500, China;
2. Institute of Basic Research in Clinical Medicine of Traditional Chinese Medicine, China Academy of Chinese Medical Science, Beijing 100700, China;
3. National D
Keywords:
Chinese medicine clinical; imbalance data classification; initial data distribution; feature selection
CLC:
TP391
DOI:
10.11992/tis.201706046
Abstract:
An algorithm based on under-sampling unbalanced data classification is a stochastic data optimization algorithm. However, in traditional Chinese medicine (TCM), it is difficult to best reflect the distribution of original clinical data to solve the problem of feature redundancy in data. Therefore, in this paper, the PRFS-FPUSAB algorithm is proposed. In the algorithm, an improved sampling method is proposed based on under-sampling. The original data distribution is reflected as much as possible; then, the classification is improved by combining integrated learning, prediction risk, and feature selection. The experimental results on meridian resistance data collected from TCM show that the algorithm improves the area under the curve, and the selected characteristics are also in accordance with TCM theory.
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