Automatically assigned DDC number: 006312

Manually assigned DDC number: 006312

Number of references: 16

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

Author:

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

Description: Twenty-two decision tree, nine statistical, and two neural network algorithms are compared on thirty-two datasets in terms of classification accuracy, training time, and (in the case of trees) number of leaves. Classification 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 significantly different from twenty other algorithms. Another statistical algorithm, logistic regression, is second and third with respect to the two accuracy criteria. The most accurate decision tree algorithm is QUEST with linear splits, which ranks fourth and fifth, 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 re...

Contributor: The Pennsylvania State University CiteSeer Archives

Publisher: unknown

Date: 1999-03-04

Pubyear: 1999

Format: ps

Identifier: http://citeseer.ist.psu.edu/169859.html

Source: http://www.cs.wisc.edu/~limt/compare.ps

Language: en

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