关键词: 相关向量机, AdaBoost算法, 信息熵, 集成学习

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 performance of RVM and has better performance on accuracy, efficiency and stability compared with AdaBoost-RVM and AdaBoost-ARVM classifiers.

Key words: Relevance Vector Machine（RVM）, AdaBoost, information entropy, ensemble learning