通信学报Issue(5)：42-51,10.DOI:10.3969/j.issn.1000-436x.2013.05.005
类不均衡的半监督高斯过程分类算法
Semi-supervised Gaussian process classification algorithm addressing the class imbalance
摘要
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 in-to 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 circums-tances 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.关键词
类不均衡/半监督/高斯过程分类/自训练Key words
class imbalance/semi-supervised/Gaussian process classification/self-training分类
信息技术与安全科学引用本文复制引用
夏战国,夏士雄,蔡世玉,万玲..类不均衡的半监督高斯过程分类算法[J].通信学报,2013,(5):42-51,10.基金项目
国家自然科学基金资助项目（50674086）；国家教育部博士点基金资助项目(20110095110010)Foundation Items：The National Natural Science Foundation of China (50674086) （20110095110010）
The Ph.D. Programs Foundation of the Ministry of Education of China (20110095110010) （20110095110010）
