The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Measuring classifier performance: a coherent alternative to the area under the ROC curve
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Cites
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
The use of the area under the ROC curve in the evaluation of machine learning algorithms
Bradley, A. P. (1997) · Pattern Recognition · doi:10.1016/s0031-3203(96)00142-2 · Cited by 7,217
A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems
Hand, D. J., & Till, R. J. (2001) · Machine Learning · doi:10.1023/a:1010920819831 · Cited by 2,276
Self-citation
Research works printed but not linked: 21
- Adams, N. M., & Hand, D. J. (1999). Comparing classifiers when the misallocation costs are uncertain. Pattern Recognition, 32, 1139–1147. · DOI · Paper page
- Dodd, L. E., & Pepe, M. S. (2003). Partial AUC estimation and regression. Biometrics, 59, 614–623. · DOI · Paper page
- Fawcett, T. (2004). ROC graphs: notes and practical considerations for researchers. Palo Alto: HP Laboratories.
- Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27, 861–874. · DOI · Paper page
- Flach, P. A. (2003). The geometry of ROC space: understanding machine learning metrics through isometrics. In Proc. 20th international conference on machine learning (ICML’03) (pp. 194–201).
- Hand, D. J. (1997). Construction and assessment of classification rules. New York: Wiley.
- Hand, D. J. (2004). Measurement theory and practice: the world through quantification. London: Arnold.
- Hand, D. J. (2005). Good practice in retail credit scorecard assessment. Journal of the Operational Research Society, 56, 1109–1117. · DOI
- Hand, D. J. (2006). Classifier technology and the illusion of progress (with discussion). Statistical Science, 21, 1–34. · DOI · Paper page
- Hanley, J. A. (1989). Receiver operating characteristic (ROC) methodology: the state of the art. Critical Reviews in Diagnostic Imaging, 29, 307–335.
- Hanley, J. A., & McNeil, B. J. (1982). The meaning and use of the area under an ROC curve. Radiology, 143, 29–36. · DOI · Paper page
- Jamain, A., & Hand, D. J. (2008). Mining supervised classification performance studies: a meta-analytic investigation. Journal of Classification, 25, 87–112. · DOI
- Krzanowski, W. J., & Hand, D. J. (2009). ROC curves for continuous data. London: Chapman and Hall. · DOI · Paper page
- McClish, D. K. (1989). Analyzing a portion of the ROC curve. Medical Decision Making, 9, 190–195. · DOI · Paper page
- Pepe, M. S. (2003). The statistical evaluation of medical tests for classification and prediction. Oxford: Oxford University Press. · DOI · Paper page
- Provost, F., & Fawcett, T. (1997). Analysis and visualization of classifier performance: Comparison under imprecise class and cost distributions. In KDD-97—third international conference on knowledge discovery and data mining.
- Provost, F., Fawcett, T., & Kohavi, R. (1998). The case against accuracy estimation for comparing induction algorithms. In Proceedings of the 15th international conference on machine learning, ICML-98.
- Rudin, W. (1964). Principles of mathematical analysis (2nd edn.). New York: McGraw-Hill.
- Scott, M. J. J., Niranjan, M., & Prager, R. W. (1998). Parcel: feature subset selection in variable cost domains (Technical Report CUED/F-INFENG/TR. 323). Cambridge University Engineering Department, UK.
- Thomas, L. C., Edelman, D. B., & Crook, J. N. (2002). Credit scoring and its applications. Philadelphia: Society for Industrial and Applied Mathematics. · DOI · Paper page
- Webb, A. (2002). Statistical pattern recognition (2nd edn.). New York: Wiley. · DOI · Paper page
Cited by
Chicco, D., & Jurman, G. (2020) · BMC Genomics · doi:10.1186/s12864-019-6413-7 · Cited by 6,143
Learning from class-imbalanced data: Review of methods and applications
Haixiang, G., Yijing, L., Shang, J., Mingyun, G., Yuanyue, H., & Bing, G. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.12.035 · Cited by 2,377
Benchmarking state-of-the-art classification algorithms for credit scoring: an update of research
Lessmann, S., Baesens, B., Seow, H.-V., & Thomas, L. C. (2015) · European Journal of Operational Research · doi:10.1016/j.ejor.2015.05.030 · Cited by 1,284
A Survey of Predictive Modeling on Imbalanced Domains
Branco, P., Torgo, L., & Ribeiro, R. P. (2017) · ACM Computing Surveys · doi:10.1145/2907070 · Cited by 1,129
A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring
Xia, Y., Liu, C., Li, Y., & Liu, N. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.02.017 · Cited by 769
Metadata
- Authors
- Hand, D. J.
- Venue
- Machine Learning
- Published
- 2009-06-16
- Volume
- 77
- Issue
- 1
- Pages
- 103-123
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W2137959503
- Cited by
- 1,003
- References
- 3
- FWCI
- 41.56
- Type
- journal article
- Language
- en
- Open access
- bronze
- Licence
- www.springer.com/tdm (tdm)
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- Hand, D. J. (2009). Measuring classifier performance: a coherent alternative to the area under the ROC curve. Machine Learning. https://doi.org/10.1007/s10994-009-5119-5
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
