Improving the ϵ-approximate algorithm for Probabilistic Classifier Chains

Probabilistic Classifier Chains are a multi-label classification method which has gained the attention of researchers in recent years. This is because of their ability to optimally estimate the entire joint conditional probability of a label combination through the product rule of probability. Their...

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Veröffentlicht in:	Knowledge and information systems Jg. 62; H. 7; S. 2709 - 2738
Hauptverfasser:	Fdez-Díaz, Miriam, Fdez-Díaz, Laura, Mena, Deiner, Montañés, Elena, Quevedo, José Ramón, Coz, Juan José del
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.07.2020 Springer Nature B.V
Schlagworte:	Algorithms Chains Classifiers Computational efficiency Computer Science Computer simulation Computing costs Computing time Conditional probability Data Mining and Knowledge Discovery Database Management Heuristic methods Information Storage and Retrieval Information Systems and Communication Service Information Systems Applications (incl.Internet) IT in Business Optimization Regular Paper Searching Statistical analysis Multi-label Inference Classifier Chains approximate algorithm
ISSN:	0219-1377, 0219-3116
Online-Zugang:	Volltext
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