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.