Neural Networks, Decision Tree Induction and Discriminant Analysis: an Empirical Comparison
作者:
CurramStephen P.,
MingersJohn,
期刊:
Journal of the Operational Research Society
(Taylor Available online 1994)
卷期:
Volume 45,
issue 4
页码: 440-450
ISSN:0160-5682
年代: 1994
DOI:10.1057/jors.1994.62
出版商: Taylor&Francis
关键词: classification;decision-trees;discriminant analysis;induction;neural networks
数据来源: Taylor
摘要:
AbstractThis paper presents an empirical comparison of three classification methods: neural networks, decision tree induction and linear discriminant analysis. The comparison is based on seven datasets with different characteristics, four being real, and three artificially created. Analysis of variance was used to detect any significant differences between the performance of the methods. There is also some discussion of the problems involved with using neural networks and, in particular, on overfitting of the training data. A comparison between two methods to prevent overfitting is presented: finding the most appropriate network size, and the use of an independent validation set to determine when to stop training the network.
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