NDLI: Decision trees work better than feed-forward back-prop neural nets for a specific class of problems

Content Provider	IEEE Xplore Digital Library
Author	Xiaomei Liu Bowyer, K.W. Hall, L.O.
Copyright Year	2004
Description	Author affiliation: Dept. of Comput. Sci. & Eng., Notre Dame Univ., USA (Xiaomei Liu; Bowyer, K.W.)
Abstract	Feed forward, back propagation neural networks are known to be universal approximators in a certain theoretical sense. They can be time-consuming to train and require significant parameter tuning. Decision trees are generally faster and simpler to train, but are widely assumed to not offer predictive accuracy as good as feed forward, back propagation neural networks. We have noticed in previous work that decision trees tended to outperform feed forward, back propagation neural networks on a certain dataset. We provide a description of a class of problems that are extremely difficult for feed forward, back propagation neural networks but relatively simple for decision trees. Experiments with synthetic datasets illustrate the class of problems. The importance of this result lies in making decisions about when to employ what type of classifier in practice.
Starting Page	5969
Ending Page	5974
File Size	478801
Page Count	6
File Format	PDF
ISBN	0780385667
ISSN	1062922X
DOI	10.1109/ICSMC.2004.1401150
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-10-10
Publisher Place	Netherlands
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Decision trees Feedforward systems Neural networks Feedforward neural networks Feeds Computer science Accuracy Testing Classification tree analysis Data engineering
Content Type	Text
Resource Type	Article

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Decision Trees Work Better Than Feed-Forward Back-Prop Neural Nets for A Specific Class of Problems ∗

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