A Coupled k-Nearest Neighbor Algorithm for Multi-label Classification
ML-\(k\)NN is a well-known algorithm for multi-label classification. Although effective in some cases, ML-\(k\)NN has some defect due to the fact that it is a binary relevance classifier which only considers one label every time. In this paper, we present a new method for multi-label classification, which is based on lazy learning approaches to classify an unseen instance on the basis of its \(k\) nearest neighbors. By introducing the coupled similarity between class labels, the proposed method exploits the correlations between class labels, which overcomes the shortcoming of ML-\(k\)NN. Experiments on benchmark data sets show that our proposed Coupled Multi-Label \(k\) Nearest Neighbor algorithm (CML-\(k\)NN) achieves superior performance than some existing multi-label classification algorithms.
KeywordsMulti-label Coupled Classification Nearest neighbor
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