Reference : Bias-variance tradeoff of soft decision trees

	Document type :	Scientific congresses and symposiums : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	To cite this reference:	http://hdl.handle.net/2268/80454

Title :	Bias-variance tradeoff of soft decision trees
Language :	English
Author, co-author :	Olaru, Cristina [ > > ]
	Wehenkel, Louis [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Publication date :	2004
Pages :	8
Audience :	International
Event name :	IPMU-04, Information Processing and Management of Uncertainty in Knowledge-Based Systems
Keywords :	[en] Machine Learning ; Fuzzy systems
Abstract :	[en] This paper focuses on the study of the error composition of a fuzzy decision tree induction method recently proposed by the authors, called soft decision trees. This error may be expressed as a sum of three types of error: residual error, bias and variance. The paper studies empirically the tradeoff between bias and variance in a soft decision tree method and compares it with the tradeoff of classical crisp regression and classification trees. The main conclusion is that the reduced prediction variance of fuzzy trees is the main reason for their improved performance with respect to crisp ones.
Target :	Researchers ; Professionals ; Students
Permalink :	http://hdl.handle.net/2268/80454

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