Multi-Label Learning Based on Label-Specific Features and Local Pairwise Label Correlation
Multi-Label Learning Based on Label-Specific Features and Local Pairwise Label Correlation
复制标题
基于标签特定特征和局部成对标签相关性的多标签学习
DOI：
10.1016/j.neucom.2017.07.044
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发表时间：
2018-01
期刊：
影响因子：
6
通讯作者：
Yun Kang
中科院分区：
文献类型：
--
作者：
Wei Weng;Yaojin Lin;Yun Kang;Yuwen Li;Yun Kang
近年来，多标签学习引起了人们的高度关注。它的任务之一是为每个实例与一组标签相关联的问题构建分类模型。为了利用鉴别特征进行分类，提出了一些方法来构造标签特定的特征。然而，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方法称为LF-LPLC的多标签学习，它集成了标签特定的功能和本地成对标签相关性的同时。首先，我们将原始特征空间转换为低维的标签特定特征空间，因此每个标签都有自己的特定表示。然后，我们利用每对标签之间的局部相关性，通过最近邻技术。根据局部相关性，每个标签的标签特定的特征是通过联合相关数据从其他标签特定的功能扩展。该框架丰富了标签的语义信息，解决了类别分布不均衡的问题。最后，对于每个标签，基于其标签特定的功能，我们构建了一个二进制分类算法来测试未标记的实例。在基准数据集的集合上进行了全面的实验。与最先进的方法的比较结果验证了我们所提出的方法的竞争力的性能。
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.
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影响因子：
6
作者：
Doquire, Gauthier;Verleysen, Michel
通讯作者：
Verleysen, Michel
DOI：
--
发表时间：
2008
期刊：
--
影响因子：
--
作者：
Konstantinos Trohidis;Grigorios Tsoumakas;George M. Kalliris;I. Vlahavas
通讯作者：
Konstantinos Trohidis;Grigorios Tsoumakas;George M. Kalliris;I. Vlahavas
DOI：
10.1109/tkde.2019.2951561
发表时间：
2019-11
影响因子：
8.9
作者：
Min-Ling Zhang;Qian-Wen Zhang;Jun-Peng Fang;Yukun Li;Xin Geng
通讯作者：
Min-Ling Zhang;Qian-Wen Zhang;Jun-Peng Fang;Yukun Li;Xin Geng
DOI：
10.1145/1015330.1015361
发表时间：
2004-07
期刊：
Proceedings of the twenty-first international conference on Machine learning
影响因子：
--
作者：
Sheng Gao;Wen Wu;Chin-Hui Lee;Tat-Seng Chua
通讯作者：
Sheng Gao;Wen Wu;Chin-Hui Lee;Tat-Seng Chua
影响因子：
6
作者：
Liu, Huawen;Wu, Xindong;Zhang, Shichao
通讯作者：
Zhang, Shichao