Heuristic classifier chains for multi-label classification

Abstract

Multi-label classification, in opposite to conventional classification, assumes that each data instance may be associated with more than one labels simultaneously. Multi-label learning methods take advantage of dependencies between labels, but this implies greater learning computational complexity. The paper considers Classifier Chain multi-label classification method, which in original form is fast, but assumes the order of labels in the chain. This leads to propagation of inference errors down the chain. On the other hand recent Bayes-optimal method, Probabilistic Classifier Chain, overcomes this drawback, but is computationally intractable. In order to find the trade off solution it is presented a novel heuristic approach for finding appropriate label order in chain. It is demonstrated that the method obtains competitive overall accuracy and is also tractable to higher-dimensional data. © 2013 Springer-Verlag Berlin Heidelberg.