@article{Read2015b,

title = {Scalable Multi-Output Label Prediction: From Classifier Chains to Classifier Trellises},

author = {Jesse Read and Luca Martino and Pablo M Olmos and David Luengo},

url = {http://www.sciencedirect.com/science/article/pii/S0031320315000084},

year  = {2015},

date = {2015-06-01},

journal = {Pattern Recognition},

volume = {48},

number = {6},

pages = {2096--2106},

abstract = {Multi-output inference tasks, such as multi-label classification, have become increasingly important in recent years. A popular method for multi-label classification is classifier chains, in which the predictions of individual classifiers are cascaded along a chain, thus taking into account inter-label dependencies and improving the overall performance. Several varieties of classifier chain methods have been introduced, and many of them perform very competitively across a wide range of benchmark datasets. However, scalability limitations become apparent on larger datasets when modeling a fully cascaded chain. In particular, the methods' strategies for discovering and modeling a good chain structure constitutes a mayor computational bottleneck. In this paper, we present the classifier trellis (CT) method for scalable multi-label classification. We compare CT with several recently proposed classifier chain methods to show that it occupies an important niche: it is highly competitive on standard multi-label problems, yet it can also scale up to thousands or even tens of thousands of labels.},

keywords = {Bayesian networks, classifer chains, Journal, Multi-label classification, multi-output prediction, structured inference},

pubstate = {published},

tppubtype = {article}

}