计算机工程与应用2019，Vol.55Issue(4)：48-55,8.DOI:10.3778/j.issn.1002-8331.1811-0325
基于标签特征和相关性的多标签分类算法
Multi-Label Classification Algorithm Based on Label-Specific Features and Label Correlation
摘要
Abstract
Aiming at the effective utilization of label specific features and label correlation, a new multi-label algorithm LSFLC is proposed, which can effectively integrate label specific features and label correlation. Firstly, for each label, new positive class instances are generated by re-sampling technology to expand the number of positive class instances. Secondly, the original feature space is transformed into a specific feature space by feature mapping function, and the label specific feature set of each label is obtained. Then, for each label, its most relevant label is found to expand its specific fea-ture set by copying the positive class instances of the label, which not only enriches the information of the label, but also solves the problem of class imbalance to a certain extent. Finally, experiments on different data sets show that the classifi-cation effect of the proposed algorithm is better.关键词
多标签学习/局部标签相关性/标签特有特征/相关实例补充Key words
multi-label learning/ local label correlation/ label-specific features/ related instances insertion分类
信息技术与安全科学引用本文复制引用
李锋,杨有龙..基于标签特征和相关性的多标签分类算法[J].计算机工程与应用,2019,55(4):48-55,8.基金项目
国家自然科学基金(No.61573266). （No.61573266）
