ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2018, Vol. 55 ›› Issue (5): 893-907.doi: 10.7544/issn1000-1239.2018.20170503

• 人工智能 •    下一篇

基于特征变权的动态模糊特征选择算法

王玲,孟建瑶   

  1. (北京科技大学自动化学院 北京 100083) (工业过程知识自动化教育部重点实验室(北京科技大学) 北京 100083) (lingwang@ustb.edu.cn)
  • 出版日期: 2018-05-01
  • 基金资助: 
    国家自然科学基金项目(61572073);中央高校基本科研业务费专项资金(FRF-BD-16-005A);北京市重点学科共建项目(XK100080537)

Dynamic Fuzzy Features Selection Based on Variable Weight

Wang Ling,Meng Jianyao   

  1. (College of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083) (Key Laboratory of Knowledge Automation for Industrial Processes (University of Science and Technology Beijing), Ministry of Education, Beijing 100083)
  • Online: 2018-05-01

摘要: 针对大多数动态特征选择算法不能实时地根据特征重要性的变化动态优化模糊特征的问题,提出了基于特征变权的动态模糊特征选择算法.该算法利用滑动窗口分割模糊化后的数据,在第1个窗口中进行离线模糊特征选择,根据输入模糊特征与输出特征的互信息量,计算各个模糊输入特征的权重,获取候选模糊特征子集,并采取后向特征选择的方式和模糊特征筛选指标得到优化模糊特征子集;在随后的窗口中进行在线模糊特征选择,结合当前窗口的候选模糊特征子集和已有模糊特征选择结果,计算模糊输入特征的重要度,获得当前窗口中的优化模糊特征子集.通过计算窗口之间模糊特征权重的变化,发现输入模糊特征的演化关系.不同数据集的仿真结果表明,所提出算法在自适应性和预测准确性方面均有显著提高.

关键词: 动态, 特征选择, 模糊, 互信息量, 权重, 滑动窗口

Abstract: In this paper, a new scheme for dynamic fuzzy feature selection based on variable weight is proposed to optimize the fuzzy feature subset with the important features dynamically. Firstly, the sliding window is adopted to divide the fuzzy dataset. In the first sliding window, the off-line fuzzy features selection algorithm is proposed to access the candidate fuzzy feature subset by calculating the weight of each fuzzy input feature according to the mutual information between the fuzzy input features and the output feature. Based on this, the optimal fuzzy feature subset are obtained by combining the backward feature selection method with the fuzzy feature selection index. With the new sliding window, the on-line fuzzy features selection algorithm is proposed, by integrating the optimal fuzzy feature selection result in the previous sliding window with the candidate fuzzy feature set in the current sliding window, the importance of the fuzzy input feature is calculated to obtain the optimal feature subset in the current window. Finally, the evolving relationship of the fuzzy input features is found with the fuzzy feature weights between the sliding windows. The simulation results show that the proposed algorithm has a significant improvement in the adaptability and prediction accuracy.

Key words: dynamic, feature selection, fuzzy, mutual information, weight, silding window

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