Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article A Dependent Multilabel Classification Method Derived from the k-Nearest Neighbor Rule | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2011 Article ID 645964 14 pages doi 2011 645964 Research Article A Dependent Multilabel Classification Method Derived from the k-Nearest Neighbor Rule Zoulficar Younes EURASIP Member 1 Fahed Abdallah 1 Thierry Denoeux 1 and Hichem Snoussi2 1Heudiasyc UMR CNRS 6599 University of Technology ofCompiegne 60205 Compiegne France 2ICD-LM2S FRE CNRS 2848 University of Technology of Troyes 10010 Troyes France Correspondence should be addressed to Zoulficar Younes Received 17 June 2010 Revised 9 January 2011 Accepted 21 February 2011 Academic Editor Bulent Sankur Copyright 2011 Zoulficar Younes et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. In multilabel classification each instance in the training set is associated with a set of labels and the task is to output a label set whose size is unknown a priori for each unseen instance. The most commonly used approach for multilabel classification is where a binary classifier is learned independently for each possible class. However multilabeled data generally exhibit relationships between labels and this approach fails to take such relationships into account. In this paper we describe an original method for multilabel classification problems derived from a Bayesian version of the k-nearest neighbor k-NN rule. The method developed here is an improvement on an existing method for multilabel classification namely multilabel k-NN which takes into account the dependencies between labels. Experiments on simulated and benchmark datasets show the usefulness and the efficiency of the proposed approach as compared to other existing methods. 1. Introduction Traditional single-label classification assigns an object to exactly one class from a set of Q .
