Name
Abstract | ||
|---|---|---|
A common way of attacking multi-label classification problems is by splitting it into a set of binary classification problems, then solving each problem independently using traditional single-label methods. Nevertheless, by learning classifiers separately the information about the relationship between labels tends to be neglected. Built on recent advances in structure learning in Ising Markov Random Fields (I-MRF), we propose a multilabel classification algorithm that explicitly estimate and incorporate label dependence into the classifiers learning process by means of a sparse convex multitask learning formulation. Extensive experiments considering several existing multi-label algorithms indicate that the proposed method, while conceptually simple, outperforms the contenders in several datasets and performance metrics. Besides that, the conditional dependence graph encoded in the I-MRF provides a useful information that can be used in a posterior investigation regarding the reasons behind the relationship between labels. |
Year | Venue | Field |
|---|---|---|
2015 | IJCAI'15 Proceedings of the 24th International Conference on Artificial Intelligence | Random field,Multi-task learning,Binary classification,Computer science,Markov chain,Structure learning,Regular polygon,Ising model,Artificial intelligence,Conditional dependence,Machine learning |
DocType | Citations | PageRank |
Conference | 2 | 0.37 |
References | Authors | |
24 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| André Ricardo Gonçalves | 1 | 16 | 6.43 |
| Fernando J. Von Zuben | 2 | 831 | 81.83 |
| Arindam Banerjee | 3 | 4716 | 233.98 |