A Ranking-based KNN Approach for Multi-Label Classification
نویسندگان
چکیده
Multi-label classification has attracted a great deal of attention in recent years. This paper presents an 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. Our experiment results demonstrate that the proposed method outperforms state-ofthe-art instance-based learning approaches. Keywords-multilabel classification; nearest neighbor classification; ranking; optimization; generalized pattern search
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