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Descripción
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| Multi-output inference tasks, such as multi-label classification, have become increasingly important in recent years. A popular method for multi-label classification is \emph{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 \key{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. | |
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Internacional
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Si |
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JCR del ISI
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Si |
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Título de la revista
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Pattern Recognition |
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ISSN
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0031-3203 |
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Factor de impacto JCR
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2,584 |
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Información de impacto
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Volumen
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48 |
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DOI
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10.1016/j.patcog.2015.01.004 |
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Número de revista
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6 |
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Desde la página
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2096 |
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Hasta la página
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2109 |
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Mes
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JUNIO |
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Ranking
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40/248 en JCR. Cat.: ENG., ELECTRICAL & ELECTRONIC |