Reference : Improving the bias/variance tradeoff of decision trees - towards soft tree induction
Scientific journals : Article
Engineering, computing & technology : Computer science
http://hdl.handle.net/2268/25741
Improving the bias/variance tradeoff of decision trees - towards soft tree induction
English
Geurts, Pierre mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Olaru, Cristina [Université de Liège - ULg > Dép. d'électricité, électronique et informatique > Systèmes et modélisation > >]
Wehenkel, Louis mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
2001
Engineering intelligent systems
9
195-204
Yes
International
[en] machine learning
[en] One of the main difficulties with standard top down induction of decision trees comes from the high variance of these methods. High variance means that, for a given problem and sample size, the resulting tree is strongly dependent on the random nature of the particular sample used for training. Consequently, these algorithms tend to be suboptimal in terms of accuracy and interpretability. This paper analyses this problem in depth and proposes a new method, relying on threshold softening, able to significantly improve the bias/variance tradeoff of decision trees. The algorithm is validated on a number of benchmark problems and its relationship with fuzzy decision tree induction is discussed. This sheds some light on the success of fuzzy decision tree induction and improves our understanding of machine learning, in general.
http://hdl.handle.net/2268/25741
http://www.montefiore.ulg.ac.be/services/stochastic/pubs/2001/GOW01

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