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Abstract: . An importantcomponent of many data mining projects is finding a good classification algorithm, a process that requires very careful thought about experimental design. If not done very carefully, comparative studies of classification and other types of algorithms can easily result in statistically invalid conclusions. This is especially true when one is using data mining techniques to analyze very large databases, which inevitably contain some statistically unlikely data. This paper describes... (Update)
Context of citations to this paper: More
...how well those algorithms perform on standard data sets (e.g. UC Irvine repository [20] or in a particular domain. As pointed out in [28], such comparisons of algorithms may be misleading since the performance of the classifiers they produce depends strongly on the specific...
...a simple, easily understood, and wellstudied distribution. Its use for evaluation has been discussed for classifiers [1] 10] 15] 41] [42] and for evaluation and tuning in a variety of other domains, including inductive learning systems [18] and speech recognition...
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BibTeX entry: (Update)
S. L. Salzberg. On comparing classifiers: A critique of current research and methods. Technical Report CS-1995-06, John Hopkins University, 1995. http://citeseer.ist.psu.edu/salzberg95comparing.html More
@article{ salzbergsalzbergcomparing,
author = "Steven Salzberg",
title = "On Comparing Classifiers: {A} Critique of Current Research and Methods",
url = "citeseer.ist.psu.edu/salzberg95comparing.html" }
Citations (may not include all citations):256 Parallel networks that learn to pronounce english text (context) - Sejnowski, Rosenberg - 1987
216 Very simple classification rules perform well on most common.. (context) - Holte - 1993
203 Multi-interval discretization of continuous valued attribute.. (context) - Fayyad, Irani - 1993
84 A conservation law for generalization performance (context) - Schaffer - 1994
82 UCI repository of machine learning databases -- a machine-re.. (context) - Murphy - 1995
70 Predicting the secondary structure of globular proteins usin.. (context) - Qian, Sejnowski - 1988
56 Generalizing from case studies: A case study - Aha - 1992
55 Symbolic and neural learning algorithms: An experimental com.. (context) - Shavlik, Mooney et al. - 1991
45 An experimental comparison of the nearest-neighbor and neare.. - Wettschereck, Dietterich - 1995
45 the connection between in-sample testing and generalization .. (context) - Wolpert - 1992
43 Bayesian model selection in social research (context) - Raftery - 1995
41 Machine learning as an experimental science (context) - Kibler, Langley - 1988
20 Experimental Designs (context) - Cochran, Cox - 1957
19 A study of experimental evaluations of neural network algori.. - Prechelt - 1995
14 Which method learns most from the data (context) - Feelders, Verkooijen - 1995
7 Knowledge discovery through induction with randomization tes.. - Jensen - 1991
6 Statistical significance in inductive learning (context) - Gascuel, Caraux - 1992
3 Statistical tests for comparing supervised learning algorith.. (context) - Dietterich - 1996
3 Review of Economics and Statistics (context) - Denton, as - 1985
2 Statistical Thinking for Behavioral Scientists (context) - Hildebrand - 1986
2 Labeling space: A tool for thinking about significance testi.. (context) - Jensen - 1995
1 Overfitting in inductive learning algorithms: Why it occurs .. (context) - Jensen, Cohen - 1996
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