计算机工程2012，Vol.38Issue(22)：167-170,175,5.
基于粒计算的K近邻多标签学习算法
K-nearest Neighbor Multi-label Learning Algorithm Based on Granular Computing
摘要
Abstract
In Multi-label K-nearest Neighbor(ML-KNN) learning algorithm, the number of nearest neighbors is given in prior and its value is chosen without considering the distribution of samples, it is possible that highly similar samples are not in the nearest neighbor or low similar samples are in the nearest neighbor set, which affect the performance of the classifier. In view of this case, a novel ML-KNN algorithm is put forward based on the idea of Granular Computing(GrC), the nearest neighbor set is constructed with the controlling of the granular hierarchy, and the nearest neighbors of a sample have high similarity and highly similar samples can be added to nearest neighbor set. Experimental results show that most of the evaluation criteria in new algorithm are better than the traditional algorithm.关键词
多标签学习/粒计算/K近邻/粒度/评价指标Key words
multi-label learning/ Granular Computing(GrC)/ K-nearest Neighbor(KNN)/ granularity/ evaluation index分类
信息技术与安全科学引用本文复制引用
陈小波,吴涛,高正龙..基于粒计算的K近邻多标签学习算法[J].计算机工程,2012,38(22):167-170,175,5.基金项目
国家自然科学基金资助项目(61073117) （61073117）
国家"973"计划基金资助项目(2007BC311003) （2007BC311003）
安徽大学学术创新团队基金资助项目(KJTD001B) （KJTD001B）
安徽大学研究生学术创新基金资助项目(yfc090008) （yfc090008）
