Abstract
Since multi-label data is ubiquitous in reality, a promising study in data mining is multi-label learning. Facing with the multi-label data, traditional single-label learning methods are not competent for the classification tasks. This paper proposes a new lazy learning algorithm for the multi-label classification. The characteristic of our method is that it takes both binary relevance and shelly neighbors into account. Unlike k nearest neighbors, the shelly neighbors form a shell to surround a given instance. As a result, our method not only identifies more helpful neighbors for classification, but also exempts from the perplexity of choosing an optimal value for k in the lazy learning methods. The experiments carried out on five benchmark datasets demonstrate that the proposed approach outperforms standard lazy multi-label classification in most cases.
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Liu, H., Zhang, S., Zhao, J., Wu, J., Zheng, Z. (2012). A New Multi-label Learning Algorithm Using Shelly Neighbors. In: Zhou, S., Zhang, S., Karypis, G. (eds) Advanced Data Mining and Applications. ADMA 2012. Lecture Notes in Computer Science(), vol 7713. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35527-1_18
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DOI: https://doi.org/10.1007/978-3-642-35527-1_18
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