ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2020, Vol. 57 ›› Issue (8): 1581-1593.doi: 10.7544/issn1000-1239.2020.20200127

所属专题: 2020数据挖掘与知识发现专题

• 人工智能 • 上一篇    下一篇

消除随机一致性的支持向量机分类方法

王婕婷,钱宇华,李飞江,刘郭庆   

  1. (山西大学大数据科学与产业研究院 太原 030006) (计算智能与中文信息处理教育部重点实验室(山西大学) 太原 030006) (山西大学计算机与信息技术学院 太原 030006) (jietingwang@email.sxu.edu.cn)
  • 出版日期: 2020-08-01
  • 基金资助: 
    国家自然科学基金项目(61672332);山西省三晋学者支持计划项目;山西省回国留学人员科研项目(2017023)

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).

摘要: 在人类自身的学习过程中,对学习结果进行科学客观的评价与反馈是关键环节.通常,由于学习者的知识缺陷或证据不足使得学习过程存在随机性,进一步可能导致学习结果与实际情况产生随机一致性.对此结果的直接反馈将严重影响学习性能的提升.同样,机器学习是以数据为驱动、以目标为导向的学习系统.由于经验历史数据有限、不平衡、含噪音等特质导致学习结果具有随机一致性.然而,以准确度为反馈准则的机器学习系统无法辨识随机一致性,这会影响学习系统的泛化能力.首先给出随机准确度和纯准确度的定义,并且进一步分析消除随机准确度的意义及必要性.然后,基于纯准确度指标,提出消除随机一致性的支持向量机分类方法PASVM,并在KEEL数据集的10种不同领域的基准测试集上验证其有效性.实验结果表明:相比于SVM、SVMperf以及其他可用于优化纯准确度指标的学习方法,PASVM泛化性能有明显提高.

关键词: 随机一致性, 纯准确度, 支持向量机, 分类, 泛化能力

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

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