Mining Supervised Classification Performance Studies: A Meta-Analytic Investigation |
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Authors: | Adrien Jamain David J Hand |
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Institution: | (1) BNP-Paribas, 10 Harewood Avenue, London, NW1 6AA, UK;(2) Department of Mathematics, and Institute for Mathematical Sciences, Imperial College, London, SW7 2AZ, UK |
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Abstract: | There have been many comparative studies of classification methods in which real datasets are used as a gauge to assess the
relative performance of the methods. Since these comparisons often yield inconclusive or limited results on how methods perform,
it is often believed that a broader approach combining these studies would shed some light on this difficult question. This
paper describes such an attempt: we have sampled the available literature and created a dataset of 5807 classification results.
We show that one of the possible ways to analyze the resulting data is an overall assessment of the classification methods,
and we present methods for that particular aim. The merits and demerits of such an approach are discussed, and conclusions
are drawn which may assist future research: we argue that the current state of the literature hardly allows large-scale investigations.
This work was sponsored by the MOD Corporate Research Programme, CISP, as part of a larger project on technology assessment.
We would like to express our appreciation to Andrew Webb for his support throughout the entire project, and to Wojtek Krzanowski
for valuable comments on a draft of this paper. We would also like to thank the anonymous referees for some very interesting
comments, some of which we hope to pursue in future work. |
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Keywords: | Classification rules Supervised classification Neural networks Tree classifiers Logistic regression Nearest neighbor method Bradley-Terry Meta-analysis Data mining |
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