A Ranking-based KNN Approach for Multi-Label Classification


Tsung-Hsien Chiang, Hung-Yi Lo, Shou-De Lin ;
Proceedings of the Asian Conference on Machine Learning, PMLR 25:81-96, 2012.


Multi-label classification has attracted a great deal of attention in recent years. This paper presents an interesting finding, namely, being able to identify neighbors with trustable labels can significantly improve the classification accuracy. Based on this finding, we propose a k-nearest-neighbor-based ranking approach to solve the multi-label classification problem. The approach exploits a ranking model to learn which neighbor’s labels are more trustable candidates for a weighted KNN-based strategy, and then assigns higher weights to those candidates when making weighted-voting decisions. The weights can then be determined by using a generalized pattern search technique. We collect several real-word data sets from various domains for the experiment. Our experiment results demonstrate that the proposed method outperforms state-of-the-art instance-based learning approaches. We believe that appropriately exploiting k-nearest neighbors is useful to solve the multi-label problem.

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