@inproceedings{e06ecfee1e574404a6170ff4484f2b5c,
title = "A coupled k-nearest neighbor algorithm for multi-label classification",
abstract = "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.",
keywords = "Classification, Coupled, Multi-label, Nearest neighbor",
author = "Chunming Liu and Longbing Cao",
year = "2015",
doi = "10.1007/978-3-319-18038-0_14",
language = "English",
isbn = "9783319180373",
series = "Lecture Notes in Computer Science",
publisher = "Springer, Springer Nature",
pages = "176--187",
editor = "Tru Cao and Ee-Peng Lim and Zhi-Hua Zhou and Tu-Bao Ho and David Cheung and Hiroshi Motoda",
booktitle = "Advances in Knowledge Discovery and Data Mining",
address = "United States",
note = "19th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2015 ; Conference date: 19-05-2015 Through 22-05-2015",
}