The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
Multivariate comparison of classification performance measures
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Citations
Cites
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
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Asuncion, A. (2007) · Medical Entomology and Zoology · Cited by 24,156
An introduction to ROC analysis
Fawcett, T. (2006) · Pattern Recognition Letters · doi:10.1016/j.patrec.2005.10.010 · Cited by 21,879
The meaning and use of the area under a receiver operating characteristic (ROC) curve.
Hanley, J. A., & McNeil, B. J. (1982) · Radiology · doi:10.1148/radiology.143.1.7063747 · Cited by 21,856
THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS
FISHER, R. A. (1936) · Annals of Eugenics · doi:10.1111/j.1469-1809.1936.tb02137.x · Cited by 15,227
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
John Lu, Z. Q. (2010) · Journal of the Royal Statistical Society Series A: Statistics in Society · doi:10.1111/j.1467-985x.2010.00646_6.x · Cited by 12,390
Applied Multivariate Statistical Analysis.
Johnson, A. R., & Wichern, D. W. (1988) · Biometrics · doi:10.2307/2531616 · Cited by 11,353
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A systematic analysis of performance measures for classification tasks
Sokolova, M., & Lapalme, G. (2009) · Information Processing & Management · doi:10.1016/j.ipm.2009.03.002 · Cited by 7,515
The use of the area under the ROC curve in the evaluation of machine learning algorithms
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Cited by
Chicco, D., & Jurman, G. (2020) · BMC Genomics · doi:10.1186/s12864-019-6413-7 · Cited by 6,143
Artificial intelligence in drug discovery: recent advances and future perspectives
Jiménez-Luna, J., Grisoni, F., Weskamp, N., & Schneider, G. (2021) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2021.1909567 · Cited by 464
Self-citationGuo, S., Popp, J., & Bocklitz, T. (2021) · Nature Protocols · doi:10.1038/s41596-021-00620-3 · Cited by 361
From the Well to the Bottle: Identifying Sources of Microplastics in Mineral Water
Weisser, J., Beer, I., Hufnagl, B., Hofmann, T., Lohninger, H., Ivleva, N. P., & Glas, K. (2021) · Water · doi:10.3390/w13060841 · Cited by 134
“[28] in MatLab (Version R2019a, The MathWorks, Inc.”
Effectiveness of molecular fingerprints for exploring the chemical space of natural products
Boldini, D., Ballabio, D., Consonni, V., Todeschini, R., Grisoni, F., & Sieber, S. A. (2024) · Journal of Cheminformatics · doi:10.1186/s13321-024-00830-3 · Cited by 97
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Metadata
- Authors
- Ballabio, D., Grisoni, F., & Todeschini, R.
- Venue
- Chemometrics and Intelligent Laboratory Systems
- Publisher
- Elsevier BV
- Published
- 2018-03
- Volume
- 174
- Pages
- 33-44
- ISSN
- 0169-7439 (ISSN-L)
- OpenAlex
- W2773275559
- Cited by
- 328
- References
- 39
- FWCI
- 10.82
- Type
- journal article
- Language
- en
- Open access
- closed
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- Ballabio, D., Grisoni, F., & Todeschini, R. (2018). Multivariate comparison of classification performance measures. Chemometrics and Intelligent Laboratory Systems. https://doi.org/10.1016/j.chemolab.2017.12.004
Data sources
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.
