Scalable multi-output label prediction: From classifier chains to classifier trellises. Issue 6 (June 2015)

• Record Type: Journal Article
• Authors: Read, Jesse; Martino, Luca; Olmos, Pablo M.; Luengo, David
• Is Part Of: Pattern recognition. Volume 48:Issue 6(2015:Jun.)
• Page Start: 2096
• Page End: 2109
• Publication Date: 2015-06
• Subjects: Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2015.01.004
• Languages: English
• ISSNs: 0031-3203

Abstract

<abstract abstract-type="author" id="ab0005"> <title id="sect0005">Abstract</title> <sec> <p id="sp0095">Multi-output inference tasks, such as multi-label classification, have become increasingly important in recent years. A popular method for multi-label classification is <italic>classifier chains</italic>, 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 modelling a fully cascaded chain. In particular, the methods׳ strategies for discovering and modelling a good chain structure constitute a major computational bottleneck. In this paper, we present the <italic>classifier trellis</italic> (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.</p> </sec> </abstract>