多标签降维中的多重代价敏感学习问题研究

项目名称： 多标签降维中的多重代价敏感学习问题研究

项目编号： No.61502058

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 其他

项目作者： 万建武

作者单位： 常州大学

项目金额： 21万元

中文摘要： 为解决多标签学习中的高维数据问题，多标签降维引起了学者关注。多标签降维主要关注标签相关性，但其难准确获取，且受多重代价敏感问题影响，即标签集上存在噪声标注、单个标签上的类别不平衡以及标签间内在的代价敏感问题。为此，本课题采用代价敏感学习方法，研究多标签降维中的多重代价敏感问题。具体如下:.1）假设标签相关性可得，对表示成对标签相关性的标签协方差矩阵、高阶标签相关性的超图，采用图学习理论，嵌入多重代价，研究代价敏感的相关性；进一步，研究标签相关性的学习，提出多标签降维和代价敏感相关性的联合学习模型，有效学习、利用相关性。.2）协同上述方法，研究“多重代价的学习+代价敏感的相关性学习”的协同学习，解决多重代价的学习问题。.本课题将发表6篇以上论文，包括CCF B类以上期刊和C类以上会议各2篇以上，国内一级学报2篇以上；申请或授权发明专利和软件著作权各1项；协助培养3名硕士研究生。

中文关键词： 多标签学习；多标签降维；代价敏感学习；标签相关性

英文摘要： To deal with the high-dimensional data in multi-label learning, multi-label dimensionality reduction attracts researchers’ attention. Multi-label dimensionality reduction mainly concerns the label correlations, however the label correlations is difficult to be obtained correctly, and may easily influenced by multiple cost sensitive problem, i.e., in the label set there is noisy annotation, class imbalance problem in the single label, and inherent cost sensitive problem between labels. Therefore, this subject researches the multiple cost sensitive problems in multi-label dimensionality reduction by adopting the cost sensitive learning methods. More specifically,.1)Assuming the label correlations can be obtained, by the graph learning theory, this subject embeds the multiple costs in label covariance matrix and hypergraph, which are used for representing pairwise label correlations and high-order label correlations respectively, and researches the cost sensitive correlations; Furthermore, to learn the label correlations, this subject proposes the joint learning model of multi-label dimensionality reduction and cost sensitive correlations, which can learn and use cost sensitive label correlations effectively..2)By fusing the methods mentioned above, this subject researches the collaborative learning model of multiple costs and cost sensitive correlations, which can solve the multiple costs learning problem. .This subject will publish more than 6 papers, including more than 2 journal papers published in or above CCF B, more than 2 international conference papers published in or above CCF C, more than 2 papers published in domestic journal; apply for or authorized 1 invention patents and 1 software copyright; assists cultivating 3 master students.

英文关键词： Multi-Label Learning;Multi-Label Dimensionality Reduction;Cost Sensitive Learning;Label Correlations