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Multi-label learning based on label-specific features and local pairwise label correlation

机译：基于标签特定功能和局部成对标签相关性的多标签学习

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摘要

Multi-label learning has drawn great attention in recent years. One of its tasks aims to build classification models for the problem where each instance associates with a set of labels. In order to exploit discriminative features for classification, some methods are proposed to construct label-specific features. However, these methods neglect the correlation among labels. In this paper, we propose a new method called LF-LPLC for multi-label learning, which integrates Label-specific features and local pairwise label correlation simultaneously. Firstly, we convert the original feature space to a low dimensional label-specific feature space, and therefore each label has a specific representation of its own. Then, we exploit the local correlation between each pair of labels by means of nearest neighbor techniques. According to the local correlation, the label-specific features of each label are expanded by uniting the related data from other label-specific features. With such a framework, it enriches the labels' semantic information and solves the imbalanced class-distribution problem. Finally, for each label, based on its label-specific features we construct a binary classification algorithm to test unlabeled instances. Comprehensive experiments are conducted on a collection of benchmark data sets. Comparison results with the state-of-the-art approaches validate the competitive performance of our proposed method. (C) 2017 Elsevier B.V. All rights reserved.
机译：近年来，多标签学习备受关注。它的任务之一是针对每个实例与一组标签相关联的问题建立分类模型。为了利用区分特征进行分类，提出了一些构造标签特定特征的方法。但是，这些方法忽略了标签之间的相关性。在本文中，我们提出了一种新的称为LF-LPLC的多标签学习方法，该方法同时集成了特定于标签的功能和局部成对标签相关性。首先，我们将原始特征空间转换为特定于低维标签的特征空间，因此每个标签都有自己的特定表示形式。然后，我们通过最近邻居技术利用每对标签之间的局部相关性。根据局部相关性，通过将来自其他标签特定功能的相关数据组合在一起，可以扩展每个标签的特定标签功能。有了这样的框架，它丰富了标签的语义信息并解决了类分配不平衡的问题。最后，对于每个标签，基于其标签特定的功能，我们构造一个二进制分类算法来测试未标签的实例。对基准数据集进行了全面的实验。与最先进方法的比较结果验证了我们提出的方法的竞争性能。 （C）2017 Elsevier B.V.保留所有权利。

著录项

  • 来源
    《Neurocomputing》 |2018年第17期|385-394|共10页
  • 作者

    Weng Wei; Lin Yaojin; Wu Shunxiang; Li Yuwen; Kang Yun;

  • 作者单位

    Xiamen Univ, Dept Automat, Xiamen 361000, Peoples R China|Xiamen Univ Technol, Coll Comp & Informat Engn, Xiamen 361024, Peoples R China;

    Minnan Normal Univ, Sch Comp Sci, Zhangzhou 363000, Peoples R China;

    Xiamen Univ, Dept Automat, Xiamen 361000, Peoples R China;

    Xiamen Univ, Dept Automat, Xiamen 361000, Peoples R China;

    Xiamen Univ, Dept Automat, Xiamen 361000, Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Multi-label learning; Label-specific features; Local pairwise label correlation;

    机译：多标签学习;标签特定功能;局部成对标签相关;

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