C4.5: programs for machine learning
C4.5: programs for machine learning
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
The Journal of Machine Learning Research
Probability Estimates for Multi-class Classification by Pairwise Coupling
The Journal of Machine Learning Research
The challenge problem for automated detection of 101 semantic concepts in multimedia
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Multilabel classification via calibrated label ranking
Machine Learning
Efficient Pairwise Classification
ECML '07 Proceedings of the 18th European conference on Machine Learning
A Unified Model for Multilabel Classification and Ranking
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Protein classification with multiple algorithms
PCI'05 Proceedings of the 10th Panhellenic conference on Advances in Informatics
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A common approach for solving multi-label classification problems using problem-transformation methods and dichotomizing classifiers is the pairwise decomposition strategy. One of the problems with this approach is the need for querying a quadratic number of binary classifiers for making a prediction that can be quite time consuming, especially in classification problems with large number of labels. To tackle this problem we propose a Dual Layer Voting Method (DLVM) for efficient pair-wise multiclass voting to the multi-label setting, which is related to the calibrated label ranking method. Five different real-world datasets (enron, tmc2007, genbase, mediamill and corel5k) were used to evaluate the performance of the DLVM. The performance of this voting method was compared with the majority voting strategy used by the calibrated label ranking method and the quick weighted voting algorithm (QWeighted) for pair-wise multi-label classification. The results from the experiments suggest that the DLVM significantly outperforms the concurrent algorithms in term of testing speed while keeping comparable or offering better prediction performance.