TY - GEN
T1 - Enhancing multi-label classification based on local label constraints and classifier chains
AU - Chen, Benhui
AU - Li, Weite
AU - Zhang, Yuqing
AU - Hu, Jinglu
N1 - Funding Information:
This research was supported by the National Natural Science Foundation of China (No. 61462003, No. 71462001), the Postdoctoral Science Foundation of China (No. 2014M550806), and the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry, PR China.
Publisher Copyright:
© 2016 IEEE.
PY - 2016/10/31
Y1 - 2016/10/31
N2 - In the multi-label classification issue, some implicit constraints and dependencies are always existed among labels. Exploring the correlation information among different labels is important for many applications. It not only can enhance the classifier performance but also can help to interpret the classification results for some specific applications. This paper presents an improved multi-label classification method based on local label constraints and classifier chains for solving multi-label tasks with large number of labels. Firstly, in order to exploit local label constraints in multi-label problem with large number of labels, clustering approach is utilized to segment training label set into several subsets. Secondly, for each label subset, local tree-structure constraints among different labels are mined based on mutual information metric. Thirdly, based on the mined local tree-structure label constraints, a variant of classifier chain strategy is implemented to enhance the multi-label learning system. Experiment results on five multi-label benchmark datasets show that the proposed method is a competitive approach for solving multi-label classification tasks with large number of labels.
AB - In the multi-label classification issue, some implicit constraints and dependencies are always existed among labels. Exploring the correlation information among different labels is important for many applications. It not only can enhance the classifier performance but also can help to interpret the classification results for some specific applications. This paper presents an improved multi-label classification method based on local label constraints and classifier chains for solving multi-label tasks with large number of labels. Firstly, in order to exploit local label constraints in multi-label problem with large number of labels, clustering approach is utilized to segment training label set into several subsets. Secondly, for each label subset, local tree-structure constraints among different labels are mined based on mutual information metric. Thirdly, based on the mined local tree-structure label constraints, a variant of classifier chain strategy is implemented to enhance the multi-label learning system. Experiment results on five multi-label benchmark datasets show that the proposed method is a competitive approach for solving multi-label classification tasks with large number of labels.
KW - Classifier chains
KW - Label constraints
KW - Multi-label classification
UR - http://www.scopus.com/inward/record.url?scp=85007170282&partnerID=8YFLogxK
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U2 - 10.1109/IJCNN.2016.7727370
DO - 10.1109/IJCNN.2016.7727370
M3 - Conference contribution
AN - SCOPUS:85007170282
T3 - Proceedings of the International Joint Conference on Neural Networks
SP - 1458
EP - 1463
BT - 2016 International Joint Conference on Neural Networks, IJCNN 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 International Joint Conference on Neural Networks, IJCNN 2016
Y2 - 24 July 2016 through 29 July 2016
ER -