A Coupled k-Nearest Neighbor Algorithm for Multi-label Classification

ML-kNN is a well-known algorithm for multi-label classification. Although effective in some cases, ML-kNN 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-kNN. Experiments on benchmark data sets show that our proposed Coupled Multi-Label k Nearest Neighbor algorithm (CML-kNN) achieves superior performance than some existing multi-label classification algorithms.