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MetaCost: A General Method for Making Classifiers Cost-Sensitive (1999)
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| Venue: | In Proceedings of the Fifth International Conference on Knowledge Discovery and Data Mining |
| Citations: | 415 - 4 self |
BibTeX
@INPROCEEDINGS{Domingos99metacost:a,
author = {Pedro Domingos},
title = {MetaCost: A General Method for Making Classifiers Cost-Sensitive},
booktitle = {In Proceedings of the Fifth International Conference on Knowledge Discovery and Data Mining},
year = {1999},
pages = {155--164},
publisher = {ACM Press}
}
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Abstract
Research in machine learning, statistics and related fields has produced a wide variety of algorithms for classification. However, most of these algorithms assume that all errors have the same cost, which is seldom the case in KDD prob- lems. Individually making each classification learner costsensitive is laborious, and often non-trivial. In this paper we propose a principled method for making an arbitrary classifier cost-sensitive by wrapping a cost-minimizing procedure around it. This procedure, called MetaCost, treats the underlying classifier as a black box, requiring no knowledge of its functioning or change to it. Unlike stratification, MetaCost is applicable to any number of classes and to arbitrary cost matrices. Empirical trials on a large suite of benchmark databases show that MetaCost almost always produces large cost reductions compared to the cost-blind classifier used (C4.5RULES) and to two forms of stratification. Further tests identify the key components of MetaCost and those that can be varied without substantial loss. Experiments on a larger database indicate that MetaCost scales well.
Keyphrases
general method metacost scale substantial loss classification learner costsensitive empirical trial cost-blind classifier black box machine learning wide variety benchmark database algorithm assume key component large suite arbitrary classifier kdd prob lem principled method cost-minimizing procedure cost matrix large cost reduction
