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Article

Partial Classifier Chains with Feature Selection by Exploiting Label Correlation in Multi-Label Classification

1
Department of Computer Science and Technology, China University of Mining and Technology, Beijing 100083, China
2
School of Software and IoT Engineering, Jiangxi University of Finance & Economics, Nanchang 330013, China
3
School of Technology and Business Studies, Dalarna University, 79188 Falun, Sweden
*
Authors to whom correspondence should be addressed.
Entropy 2020, 22(10), 1143; https://doi.org/10.3390/e22101143
Received: 17 August 2020 / Revised: 4 October 2020 / Accepted: 6 October 2020 / Published: 10 October 2020
Multi-label classification (MLC) is a supervised learning problem where an object is naturally associated with multiple concepts because it can be described from various dimensions. How to exploit the resulting label correlations is the key issue in MLC problems. The classifier chain (CC) is a well-known MLC approach that can learn complex coupling relationships between labels. CC suffers from two obvious drawbacks: (1) label ordering is decided at random although it usually has a strong effect on predictive performance; (2) all the labels are inserted into the chain, although some of them may carry irrelevant information that discriminates against the others. In this work, we propose a partial classifier chain method with feature selection (PCC-FS) that exploits the label correlation between label and feature spaces and thus solves the two previously mentioned problems simultaneously. In the PCC-FS algorithm, feature selection is performed by learning the covariance between feature set and label set, thus eliminating the irrelevant features that can diminish classification performance. Couplings in the label set are extracted, and the coupled labels of each label are inserted simultaneously into the chain structure to execute the training and prediction activities. The experimental results from five metrics demonstrate that, in comparison to eight state-of-the-art MLC algorithms, the proposed method is a significant improvement on existing multi-label classification. View Full-Text
Keywords: multi-label classification; classifier chains; label correlation; feature selection multi-label classification; classifier chains; label correlation; feature selection
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MDPI and ACS Style

Wang, Z.; Wang, T.; Wan, B.; Han, M. Partial Classifier Chains with Feature Selection by Exploiting Label Correlation in Multi-Label Classification. Entropy 2020, 22, 1143. https://doi.org/10.3390/e22101143

AMA Style

Wang Z, Wang T, Wan B, Han M. Partial Classifier Chains with Feature Selection by Exploiting Label Correlation in Multi-Label Classification. Entropy. 2020; 22(10):1143. https://doi.org/10.3390/e22101143

Chicago/Turabian Style

Wang, Zhenwu, Tielin Wang, Benting Wan, and Mengjie Han. 2020. "Partial Classifier Chains with Feature Selection by Exploiting Label Correlation in Multi-Label Classification" Entropy 22, no. 10: 1143. https://doi.org/10.3390/e22101143

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