Abstract
A simple yet practical multi-label learning method, called label powerset (LP), considers each different combination of labels that appear in the training set as a different class value of a single-label classification task. However, because those classes source from multiple labels, there may be some inherent relationships among them. To tackle this challenge, we propose a novel model which aims to co-learn binary classifiers, by combining the training of binary classifiers and the characterizing the relationship among them into a unified objective function. In addition, we develop an alternating optimization algorithm to solve the proposed problem. Extensive experimental results on various kinds of datasets well demonstrate the effectiveness of the proposed model.
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This work was supported by NSF China (No. 61473302, 61503396).
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Shan, J., Hou, C., Zhuge, W., Yi, D. (2018). Co-learning Binary Classifiers for LP-Based Multi-label Classification. In: Peng, Y., Yu, K., Lu, J., Jiang, X. (eds) Intelligence Science and Big Data Engineering. IScIDE 2018. Lecture Notes in Computer Science(), vol 11266. Springer, Cham. https://doi.org/10.1007/978-3-030-02698-1_39
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