计算机工程与应用2018，Vol.54Issue(5)：138-143,6.DOI:10.3778/j.issn.1002-8331.1610-0104
基于信息熵的RVM-AdaBoost组合分类器
Information entropy-based RVM-AdaBoost ensemble classifier
摘要
Abstract
In the light of the problem that AdaBoost works poorly with RVM, a new classifier which is composed of RVM and AdaBoost is proposed.The information entropy of the samples is defined by the output posterior probability of RVM. The higher is the information entropy, the samples are more easily mistaken. Use adaptive information entropy threshold to filter data and use ensemble classifier to classify the samples which are filtered.Regarding the few samples which are not filtered and have false classification results as noise data improves classifier's stability and avoids classifier's degradation. Experimental results based on UCI data sets show that the new classifier effectively improves the perfor-mance of RVM and has better performance on accuracy,efficiency and stability compared with AdaBoost-RVM and AdaBoost-ARVM classifiers.关键词
相关向量机/AdaBoost算法/信息熵/集成学习Key words
Relevance Vector Machine(RVM)/AdaBoost/information entropy/ensemble learning分类
信息技术与安全科学引用本文复制引用
翟夕阳,王晓丹,李睿,贾琪..基于信息熵的RVM-AdaBoost组合分类器[J].计算机工程与应用,2018,54(5):138-143,6.基金项目
国家自然科学基金(No.60975026,No.61273275). （No.60975026,No.61273275）
