Classifier Chains for Multi-label Classification
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Multilabel classification using error correction codes
ISICA'10 Proceedings of the 5th international conference on Advances in computation and intelligence
An efficient multi-label support vector machine with a zero label
Expert Systems with Applications: An International Journal
Fast multi-label core vector machine
Pattern Recognition
Hamming selection pruned sets (HSPS) for efficient multi-label video classification
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
Multi-label classification by exploiting label correlations
Expert Systems with Applications: An International Journal
| Hi-index | 0.02 |
Multi-label classification problem is a further generalization of traditional multi-class learning problem. In multi-label case the classes are not mutually exclusive and any sample may belong to several classes at the same time. Such problems occur in many important applications (in bioinformatics, text categorization, intrusion detection, etc.). In this paper we propose a new method for solving multi-label learning problem, based on paired comparisons approach. In this method each pair of possibly overlapping classes is separated by two probabilistic binary classifiers, which isolate the overlapping and non-overlapping areas. Then individual probabilities generated by binary classifiers are combined together to estimate final class probabilities fitting extended Bradley-Terry model with ties. Experimental performance evaluation on well-known multi-label benchmark datasets has demonstrated the outstanding accuracy results of the proposed method.