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An Ensemble Method for High-Dimensional Multilabel Data

机译：高维多标签数据的集成方法

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摘要

Multilabel learning is now receiving an increasing attention from a variety of domains and many learning algorithms have been witnessed. Similarly, the multilabel learning may also suffer from the problems of high dimensionality, and little attention has been paid to this issue. In this paper, we propose a new ensemble learning algorithms for multilabel data. The main characteristic of our method is that it exploits the features with local discriminative capabilities for each label to serve the purpose of classification. Specifically, for each label, the discriminative capabilities of features on positive and negative data are estimated, and then the top features with the highest capabilities are obtained. Finally, a binary classifier for each label is constructed on the top features. Experimental results on the benchmark data sets show that the proposed method outperforms four popular and previously published multilabel learning algorithms.
机译：现在，多标签学习越来越受到来自各个领域的关注，并且已经见证了许多学习算法。同样，多标签学习也可能会遇到高维度的问题，对此问题的关注很少。在本文中，我们提出了一种新的针对多标签数据的集成学习算法。我们方法的主要特点是，它利用每个标签的具有本地判别能力的特征来达到分类的目的。具体地，对于每个标签，估计正数据和负数据上的特征的区分能力，然后获得具有最高能力的顶部特征。最后，在最重要的特征上构造每个标签的二进制分类器。在基准数据集上的实验结果表明，该方法优于四种流行的和先前发布的多标签学习算法。

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  • 来源
    《Mathematical Problems in Engineering》 |2013年第14期|208051.1-208051.5|共5页
  • 作者

    Huawen Liu; Zhonglong Zheng; Jianmin Zhao; Ronghua Ye;

  • 作者单位

    College of Mathematics, Physics and Information Engineering, Zhejiang Normal University, Jinhua 321004, China,NCM1S, Academy of Mathematics and Systems Science, CAS, Beijing 100190, China;

    College of Mathematics, Physics and Information Engineering, Zhejiang Normal University, Jinhua 321004, China;

    College of Mathematics, Physics and Information Engineering, Zhejiang Normal University, Jinhua 321004, China;

    College of Mathematics, Physics and Information Engineering, Zhejiang Normal University, Jinhua 321004, China;

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