## An Empirical Comparison of Decision Trees and Other Classification Methods (1998)

Citations: | 14 - 1 self |

### BibTeX

@TECHREPORT{Lim98anempirical,

author = {Tjen-sien Lim and Wei-yin Loh and Yu-Shan Shih},

title = {An Empirical Comparison of Decision Trees and Other Classification Methods},

institution = {},

year = {1998}

}

### OpenURL

### Abstract

Twenty two decision tree, nine statistical, and two neural network classifiers are compared on thirtytwo datasets in terms of classification error rate, computational time, and (in the case of trees) number of terminal nodes. It is found that the average error rates for a majority of the classifiers are not statistically significant but the computational times of the classifiers differ over a wide range. The statistical POLYCLASS classifier based on a logistic regression spline algorithm has the lowest average error rate. However, it is also one of the most computationally intensive. The classifier based on standard polytomous logistic regression and a decision tree classifier using the QUEST algorithm with linear splits have the second lowest average error rates and are about 50 times faster than POLYCLASS. Among decision tree classifiers with univariate splits, the classifiers based on the C4.5, IND-CART, and QUEST algorithms have the best combination of error rate and speed, althoug...

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