Abstract
In this paper, we propose a Simplified Constraints Rank-SVM (SCRank-SVM) for multi-label classification based on well established Rank-SVM algorithm. Based on the features of the application, we remove the bias term b and modify the decision boundary. Due to the absence of term b, SCRank-SVM has milder optimization constraints. Therefore, SCRank-SVM achieves better solution space compared with Rank-SVM. Experimental results on five datasets show that the proposed algorithm is a powerful candidate for multi-label classification, compared with four existing state of the art multi-label algorithms according to four indicative measures.
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© 2014 Springer-Verlag Berlin Heidelberg
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Wang, J., Feng, J., Sun, X., Chen, SS., Chen, B. (2014). Simplified Constraints Rank-SVM for Multi-label Classification. In: Li, S., Liu, C., Wang, Y. (eds) Pattern Recognition. CCPR 2014. Communications in Computer and Information Science, vol 483. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45646-0_23
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DOI: https://doi.org/10.1007/978-3-662-45646-0_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45645-3
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