ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2020, Vol. 57 ›› Issue (8): 1581-1593.doi: 10.7544/issn1000-1239.2020.20200127

Special Issue: 2020数据挖掘与知识发现专题

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Support Vector Machine with Eliminating the Random Consistency

Wang Jieting, Qian Yuhua, Li Feijiang, Liu Guoqing   

  1. (Institute of Big Data Science and Industry, Shanxi University, Taiyuan 030006) (Key Laboratory of Computational Intelligence and Chinese Information Processing (Shanxi University), Ministry of Education, Taiyuan 030006) (School of Computer and Information Technology, Shanxi University, Taiyuan 030006)
  • Online:2020-08-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (61672332), the Program for the San Jin Young Scholars of Shanxi, and the Overseas Returnee Research Program of Shanxi Province (2017023).

Abstract: During the process of human learning, it is an important step to make the evaluation and feedback of the learning results objective. Usually, due to the lack of knowledge of evidence, there may exist consistency generated by the randomness in the learning results. Such rough feedback will hinder the improvement of the learning ability. Similarly, the machine learning system is a system driven by data and guided by performance measure. Due to the limitation, imbalance and noise of data, the results of machine learning also contain random consistency. However, the machine learning systems with the accuracy as the feedback index cannot discriminate the random consistency, which damages the generalization ability. In this paper, we propose the definition of the random accuracy and the pure accuracy. Further, the necessity of the elimination of random accuracy is analyzed. Then, based on the defined pure accuracy, we propose an SVM model with eliminating the random consistency, called as PASVM, and validate its efficiency on ten different benchmark data sets downloaded from KEEL. The experimental results show that the performance of the PASVM is better than that of the traditional SVM method, the SVMperf method and other methods that can optimize the pure accuracy measure.

Key words: random consistency, pure accuracy, support vector machine (SVM), classification, generalization ability

CLC Number: