BIASED LOCALITY-SENSITIVE SUPPORT VECTOR MACHINE BASED ON DENSITY FOR POSITIVE AND UNLABELED EXAMPLES LEARNING

摘要：

　　Learning from positive and unlabeled examples (PU learning) has been a hot topic for classification in machine learning.The key feature of this problem is that there is no labeled negative training data,which makes the traditional classification techniques inapplicable.According to this feature,we propose an algorithm called biased locality-sensitive support vector machine based on density (BLSBD-SVM) for PU learning which takes unlabeled examples as negative examples with noise.Our approach as the variant of Locality-Sensitive support vector machine (LSSVM) not only has a lot of advantages of local learning,but also makes good use of the prior information of training examples by adding the relative density degrees of training points.The experimental results on bioinformatics data show the effectiveness of our algorithm.

关键词： PU learning Locality-Sensitive density support vector machine

作者: Lujia Song Bing Yang Ting Ke Xinbin Zhao Ling Jing

作者单位: Department of Applied MathematicsCollege of Science, China Agricultural University100083, Beijing, P.R. China

会议类型: 国际会议

会议名称: 11th International Symposium on Operations Research and its Applications(第11届运筹学及其应用国际研讨会)

会议地点: 安徽黄山

会议语种:英文

页码: 188-193

在线出版日期: 2013-08-23（万方平台首次上网日期，不代表论文的发表时间）