Safe RuleFit: Learning Optimal Sparse Rule Model by Meta Safe Screening

We consider the problem of learning a sparse rule model , a prediction model in the form of a sparse linear combination of rules, where a rule is an indicator function defined over a hyper-rectangle in the input space. Since the number of all possible such rules is extremely large, it has been compu...

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Veröffentlicht in:	IEEE Transactions on Pattern Analysis and Machine Intelligence Jg. 45; H. 2; S. 2330 - 2343
Hauptverfasser:	Kato, Hiroki, Hanada, Hiroyuki, Takeuchi, Ichiro
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.02.2023 Institute of Electrical and Electronics Engineers (IEEE) The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Analytical models combinatorial algorithms convex programming Dictionaries knowledge representation formalisms and methods Learning Machine learning Numerical models Pattern analysis Prediction models Predictive models Random forests Regression tree analysis Regularization Screening
ISSN:	0162-8828, 1939-3539, 1939-3539, 2160-9292
Online-Zugang:	Volltext
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