一种增强差异性的半监督协同分类算法

半监督学习中的Tri-Training算法打破了以往算法对充分冗余视图的限制,并通过利用三个分类器处理标记置信度和样本预测问题提高了标记效率.为进一步增强协同训练过程中分类器之间的差异性以提高性能,本文在其理论基础上提出了一种增强差异性的半监督协同分类算法.该算法利用三个不同的分类器进行学习;考虑到分类模型在更新过程中,可能会因随机抽样导致性能恶化,该算法利用基于标记类别的分层抽样法来对已标记样本集进行抽样,并通过基于分类正确率的加权投票法实现了分类器的集成,提高了预测准确率.本文通过实验对所提出算法与Tri-Training算法做了性能比较,实验结果表明本文所提出的方法在分类问题上具有较好的性能,验证了该算法的有效性和可行性.

A Semi-supervised Collaboration Classification Algorithm with Enhanced Difference

Tri-Training algorithm in semi-supervised learning broke the restriction of previous algorithms on sufficient and redundant views and raised label efficiency by applying three classifiers to deal with labeled confidence.In order to further improve classifiers' performance through enhancing the difference between them,a semi-supervised collaborative classification algorithm with enhanced difference that applies three different classifiers was presented in this paper.Taking the might-be performance deterioration led by random sampling during the updating of classifying models into consideration,a method of stratified sampling based on labeled class was used by the algorithm to sample from the labeled sample sets,and the method of weighted voting based on classification accuracy realized the classifier ensemble,as a result the prediction accuracy is raised.Performance comparison between the proposed algorithm and Tri-Training algorithm was made through experiments,and the results show effectiveness of the former.