摘要
针对复杂工业过程样本集中的类不平衡、样本标注代价昂贵和样本孤点的问题,研究基于委员会投票选择(MQBC)和代价敏感支持向量机(CS-SVM)的故障检测方法.给出未标注样本信息度的定义,提出改进的委员会投票选择算法.主动代价敏感学习通过MQBC选择信息度高的未标注样本对其标注并添加到训练集.CS-SVM将不同类样本的误分类赋予不同的误分类代价,从而提高CS-SVM的故障检测率.最后,以铜闪速熔炼过程为例,实验结果验证了所提出方法的有效性.
Fault detection based on modified query by committee（MQBC） and cost-sensitive support vector machine（CS- SVM） is proposed to solve three difficulties of fault detection, including class-imbalanced dataset, expensive labeled cost and outlier of sample set. The definition of information is given and the MQBC is proposed. The score of unlabeled sample is evaluated by using information, and the high score of unlabeled sample is selected to be labeled and added to the training set. Different misclassification types of samples are given to different misclassification cost in CS-SVM, so that the fault detection rate is increased. Finally, fault detection for copper flash smelting process is studied to illustrate the effectiveness of the proposed approach.
出处
《控制与决策》
EI
CSCD
北大核心
2012年第10期1489-1493,共5页
Control and Decision
基金
国家杰出青年科学基金项目(61025015)
国家自然科学基金项目(60874069)
国家863计划项目(2009AA04Z137)
中南大学优秀博士学位论文扶植项目(2010年)
