Abstract
In recent years, more and more scholars have begun to study the problem of multi-label learning. In this paper, we propose a multi-label learning method called KM\(^{3}\)SVM based on Clustering idea. First, for each label, KM\(^{3}\)SVM method selects positive samples and negative samples, then uses clustering method to gain a number of training subsets. By using these relatively smaller and more balanced training subsets, we can get a group of classifiers. Given an unseen sample, we can obtain a series of outputs and combine them by two simple principles. Experimental results on two datasets demonstrate the superiority of the proposed method KM\(^{3}\)SVM over several state-of-the-art multi-label learning algorithms.
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Zheng, X., Fang, X., Tan, Y., Meng, L., Zhang, H. (2019). KM\(^{3}\)SVM: A Efficient Min-Max Modular Support Vector Machine Based on Clustering. In: Sun, X., Pan, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2019. Lecture Notes in Computer Science(), vol 11634. Springer, Cham. https://doi.org/10.1007/978-3-030-24271-8_42
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DOI: https://doi.org/10.1007/978-3-030-24271-8_42
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