Better decisions through science.
By J. Swets, Robyn M. Dawes, J. Monahan. Scientific American, 2000
592 citations recorded by the source corpus.
Subfields: Information Systems and Management
View Paper: https://www.semanticscholar.org/paper/bbc36cae543b73fd7adb3183c4a686ac6373b63e
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In Information Systems and Management
Citations in the corpus
- Comparison of Several Molecular Docking Programs: Pose Prediction and Virtual Screening Accuracy (2009, unknown): “Use of receiver operating characteristic (ROC) curves is widespread in other fields 77 and is gaining acceptance for the evaluation of modeling methods 17,68,78 - 80 and structure-based virtual screening.”
- Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction (2003, background): “…a representation suitable for learning, as well as the cost of computer hardware, the cost associated with the time it takes to l arn from the data, and the “opportunity cost” associated with suboptimal learning from extremely large data sets due to limited computational resources (Turney, 2000).”
- Discrimination capacity in species distribution models depends on the representativeness of the environmental domain (2013, methodology): “The area under the receiver operating characteristic (ROC) curve (AUC) has been a widely adopted statistic in measuring discrimination power (Hilden, 1991; Swets et al., 2000; Lobo et al., 2008; for extensive details on the ROC analysis see Krzanowski & Hand, 2009).”
- Ecological risk to aquatic systems from salinity increases (2003, methodology): “1993; Stewart and Melchers 1997) and ROC curves (Swets et al. 2000).”
- An introduction to ROC analysis (2006, methodology): “Swets et al. (2000) brought ROC curves to the attention of the wider public with their Scientific American article.”
- Regression Error Characteristic Curves (2003, background): “Receiver Operating Characteristic (ROC) curves have proven to be a valuable way to evaluate the quality of a discriminant function for classification problems (Egan, 1975; Swets et al., 2000; Fawcett, 2003).”
- ROC Graphs: Notes and Practical Considerations for Researchers (2007, methodology): “ROC graphs have long been used in signal detection theory to depict the tradeoff between hit rates and false alarm rates of classifiers (Egan, 1975; Swets et al., 2000).”
- Learning Decision Trees Using the Area Under the ROC Curve (2002, methodology): “If the cost parameters are not known at training time, Receiver Operating Characteristic (ROC) analysis can be applied (Provost & Fawcett 1997; Swets, Dawes & Monahan 2000).”
- 3D-Jury: A Simple Approach to Improve Protein Structure Predictions (2003, methodology): “The performance of the 3D-Jury system in LiveBench-6 The table shows the sensitivity of several structure prediction servers on 32 easy (EASY) and 64 difficult (HARD) targets and the specificity score (ROC; Swets et al., 2000) computed on all 96 targets.”
- Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers (2001, background): “Lift charts are isomorphic to ROC curves (Swets et al., 2000), but more widely used in commercial applications of probability estimation.”
- Areas beneath the relative operating characteristics (ROC) and relative operating levels (ROL) curves: Statistical significance and interpretation (2002, background): “Green and Swets 1966; Swets 1973, 1979, 1988, 1995; Egan 1975; Metz 1978; Falmagne 1985; Begg 1991; Swets et al. 2000 )†, and is being applied increasingly in the atmospheric sciences (e.g.”
- PRIE: a system for generating rulelists to maximize ROC performance (2008, methodology): “ROC graphs have long been used in signal detection theory to depict the trade-off between hit rates and false alarm rates of classifiers (Egan 1975; Swets et al. 2000).”
- Focused identification of germplasm strategy (FIGS) detects wheat stem rust resistance linked to environmental variables (2011, methodology): “Comparisons between models were made using the metric parameters derived from a confusion-matrix table and the Area Under the Curve (AUC) of the Receiver Operating Characteristics (ROC) (Swets et al. 2000; Fawcett 2006).”