ISSN 1000-1239 CN 11-1777/TP

• 人工智能 •

### 面向标记分布学习的标记增强

1. (东南大学计算机科学与工程学院 南京 211189) (计算机网络和信息集成教育部重点实验室(东南大学) 南京 211189) (软件新技术与产业化协同创新中心(南京大学) 南京 210093) (无线通信技术协同创新中心(东南大学) 南京 211189) (xgeng@seu.edu.cn)
• 出版日期: 2017-06-01
• 基金资助:
国家自然科学基金优秀青年科学基金项目(61622203)；江苏省自然科学基金杰出青年基金项目(BK20140022)

### Label Enhancement for Label Distribution Learning

Geng Xin, Xu Ning, Shao Ruifeng

1. (School of Computer Science and Engineering, Southeast University, Nanjing 211189) (Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing 211189) (Collaborative Innovation Center of Novel Software Technology and Industrialization (Nanjing University), Nanjing 210093) (Collaborative Innovation Center of Wireless Communications Technology (Southeast University), Nanjing 211189)
• Online: 2017-06-01

Abstract: Multi-label learning (MLL) deals with the case where each instance is associated with multiple labels. Its target is to learn the mapping from instance to relevant label set. Most existing MLL methods adopt the uniform label distribution assumption, i.e., the importance of all relevant (positive) labels is the same for the instance. However, for many real-world learning problems, the importance of different relevant labels is often different. For this issue, label distribution learning (LDL) has achieved good results by modeling the different importance of labels with a label distribution. Unfortunately, many datasets only contain simple logical labels rather than label distributions. To solve the problem, one way is to transform the logical labels into label distributions by mining the hidden label importance from the training examples, and then promote prediction precision via label distribution learning. Such process of transforming logical labels into label distributions is defined as label enhancement for label distribution learning. This paper first proposes the concept of label enhancement with a formal definition. Then, existing algorithms that can be used for label enhancement have been surveyed, and compared in the experiments. Results of the experiments reveal that label enhancement can effectively discover the difference of the label importance hidden in the data, and improve the performance of multi-label learning.