类不均衡的半监督高斯过程分类算法_夏战国.pdf

34 5 Vol.34 No. 5 2013 5 Journal on Communications May 2013 doi:10.3969/j.issn.1000-436x.2013.05.005 221116 TP181 A 1000-436X(2013)05-0042-10 Semi-supervised Gaussian process classification algorithm addressing the class imbalance XIA Zhan-guo, XIA Shi-xiong, CAI Shi-yu, WAN Ling (School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China) Abstract: The traditional supervised learning is difficult to deal with real-world datasets with less labeled information when the training sets class is imbalanced. Therefore, a new semi-supervised Gaussian process classification of address- ing was proposed. The semi-supervised Gaussian process was realized by calculating the posterior probability to obtain more accurate and credible labeled data, and embarking from self-training semi-supervised methods to add class label into the unlabeled data. The algorithm makes the distribution of training samples relatively balance, so the classifier can adaptively optimized to obtain better effect of classification. According to the experimental results, when the circum- stances of training set are class imbalance and much lack of label information, The algorithm improves the accuracy by obtaining effective labeled in comparison with other related works and provides a new idea for addressing the class im- balance is demonstrated. Key words: class