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Ten More Years of Error Rate Research

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  • Rosa A. Schiavo
  • David J. Hand

Abstract

The assessment of the performance of supervised classification rules by estimating their error rate (the proportion of objects misclassified) is an important area of work in statistical pattern recognition. This paper reviews the last ten years of error rate research, bringing up to date the reviews of Hand (1986a) and McLachlan (1987). Since those surveys were published, old estimators have been improved new estimators have been introduced, and new approaches to error rate estimation have been developed. Some of this work has led to deep insights into classification methodology and statistical modelling in general.

Suggested Citation

  • Rosa A. Schiavo & David J. Hand, 2000. "Ten More Years of Error Rate Research," International Statistical Review, International Statistical Institute, vol. 68(3), pages 295-310, December.
    • Handle: RePEc:bla:istatr:v:68:y:2000:i:3:p:295-310
    DOI: 10.1111/j.1751-5823.2000.tb00332.x
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    Cited by:

    1. Dean Fantazzini & Yufeng Xiao, 2023. "Detecting Pump-and-Dumps with Crypto-Assets: Dealing with Imbalanced Datasets and Insiders’ Anticipated Purchases," Econometrics, MDPI, vol. 11(3), pages 1-73, August.
    2. Airola, Antti & Pahikkala, Tapio & Waegeman, Willem & De Baets, Bernard & Salakoski, Tapio, 2011. "An experimental comparison of cross-validation techniques for estimating the area under the ROC curve," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1828-1844, April.
    3. Artem Sokolov & Daniel E Carlin & Evan O Paull & Robert Baertsch & Joshua M Stuart, 2016. "Pathway-Based Genomics Prediction using Generalized Elastic Net," PLOS Computational Biology, Public Library of Science, vol. 12(3), pages 1-23, March.
    4. Kim, Ji-Hyun, 2009. "Estimating classification error rate: Repeated cross-validation, repeated hold-out and bootstrap," Computational Statistics & Data Analysis, Elsevier, vol. 53(11), pages 3735-3745, September.
    5. Conde David & Salvador Bonifacio & Rueda Cristina & Fernández Miguel A., 2013. "Performance and estimation of the true error rate of classification rules built with additional information. An application to a cancer trial," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 12(5), pages 583-602, October.

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