A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-three Old and New Classification Algorithms

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A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-three Old and New Classification Algorithms (2000)

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by Tjen-Sien Lim , WEI-YIN LOH , W. Cohen

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@MISC{Lim00acomparison,
    author = {Tjen-Sien Lim and WEI-YIN LOH and W. Cohen},
    title = {A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-three Old and New Classification Algorithms},
    year = {2000}
}

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Abstract

. Twenty-two decision tree, nine statistical, and two neural network algorithms are compared on thirty-two datasets in terms of classication accuracy, training time, and (in the case of trees) number of leaves. Classication accuracy is measured by mean error rate and mean rank of error rate. Both criteria place a statistical, spline-based, algorithm called Polyclass at the top, although it is not statistically signicantly dierent from twenty other algorithms. Another statistical algorithm, logistic regression, is second with respect to the two accuracy criteria. The most accurate decision tree algorithm is Quest with linear splits, which ranks fourth and fth, respectively. Although spline-based statistical algorithms tend to have good accuracy, they also require relatively long training times. Polyclass, for example, is third last in terms of median training time. It often requires hours of training compared to seconds for other algorithms. The Quest and logistic regression algor...

Keyphrases

training time prediction accuracy new classification algorithm thirty-three old classication accuracy accuracy criterion logistic regression algor good accuracy median training time twenty-two decision tree spline-based statistical algorithm thirty-two datasets accurate decision tree algorithm linear split criterion place statistical algorithm twenty algorithm mean rank neural network algorithm long training time mean error rate logistic regression error rate

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