Robust Classification for Imprecise Environments

@article{Provost2004RobustCF,
  title={Robust Classification for Imprecise Environments},
  author={F. Provost and Tom Fawcett},
  journal={Machine Learning},
  year={2004},
  volume={42},
  pages={203-231}
}
In real-world environments it usually is difficult to specify target operating conditions precisely, for example, target misclassification costs. This uncertainty makes building robust classification systems problematic. We show that it is possible to build a hybrid classifier that will perform at least as well as the best available classifier for any target conditions. In some cases, the performance of the hybrid actually can surpass that of the best known classifier. This robust performance… Expand
1,242 Citations
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  • 90
  • Highly Influenced
  • PDF
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  • 25
  • PDF
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  • 2
  • PDF
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  • 3
  • PDF
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  • 76
  • PDF
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  • 2
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  • 142
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References

SHOWING 1-10 OF 46 REFERENCES
MetaCost: a general method for making classifiers cost-sensitive
  • 1,421
  • PDF
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
  • 2,997
  • PDF
Detecting Concept Drift with Support Vector Machines
  • 472
  • PDF
C4.5: Programs for Machine Learning
  • 21,067
The Case against Accuracy Estimation for Comparing Induction Algorithms
  • 1,152
  • PDF
On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
  • S. Salzberg
  • Computer Science
  • Data Mining and Knowledge Discovery
  • 2004
  • 834
  • PDF
A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection
  • 10,090
  • PDF
Reducing Misclassification Costs
  • 350
  • PDF
...
1
2
3
4
5
...