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HEIV. All measurements are noisy.
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Classification; Multiple classes; Receiver operating characteristic
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Area under the curve; Classification; Gini coefficient; ROC curve;
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Multiclass classification
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Focusing, Medical diagnosis, Optimization, Pathology, Protocols,
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Biomedical imaging
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ROC curves
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Binary classification, Classifier analysis, Detection theory,
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Signal detection, Correlation, Mathematical model, Robustness,
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Elsevier DOI
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Receiver operating characteristic (ROC), Indeterminacy in classification
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GeoRS(58), No. 11, November 2020, pp. 8093-8115.
IEEE DOI
2011
Hyperspectral imaging, Support vector machines, Detectors,
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An Effective Evaluation Tool for Hyperspectral Target Detection:
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IEEE DOI
2106
Detectors,
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Elsevier DOI
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AUC ROC, ARatio, Metrics, Probabilistic labels, Confusion matrix
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PAMI(44), No. 3, March 2022, pp. 1385-1398.
IEEE DOI
2202
Area Under roc Curve.
Kernel, Stochastic processes, Training, Approximation algorithms,
Optimization, Measurement, Learning systems, AUC maximization,
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IET-CV(16), No. 2, 2022, pp. 192-203.
DOI Link
2202
ER-loss, error analysis, image classification, similar features, softmax
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IEEE DOI
2205
Task analysis, Observers, Estimation, Monte Carlo methods, Training,
Signal detection, Multitasking, Numerical observers, deep learning
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2210
Area under the ROC.
Measurement, Machine learning, Complexity theory,
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Deep ROC Analysis and AUC as Balanced Average Accuracy, for Improved
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PAMI(45), No. 1, January 2023, pp. 329-341.
IEEE DOI
2212
Sensitivity, Area measurement, Hospitals, Predictive models,
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PAMI(45), No. 8, August 2023, pp. 10228-10246.
IEEE DOI
2307
Optimization, Upper bound, Linear programming, Training, Deep learning,
Standards, Optimization methods, AUC Optimization, partial AUC
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2311
Area Under the ROC curve.
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Elsevier DOI
2402
Binary classification receiver operating characteristic (ROC), ANOVA
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2406
AUC: Area under ROC Curve.
Optimization, Noise measurement, Task analysis, Supervised learning,
Semisupervised learning, Training, weakly supervised learning
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Xu, Q.Q.[Qian-Qian],
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Algorithm-Dependent Generalization of AUPRC Optimization:
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IEEE DOI
2406
Optimization, Stability analysis, Stochastic processes,
Measurement, Standards, Approximation algorithms, stability
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Kolmogorov-Smirnov and ROC curve metrics for binary classification
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ICPR22(1194-1199)
IEEE DOI
2212
Measurement, Decision support systems, Power measurement,
Decision making, Key performance indicator, Space transformation matrix
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Sani, D.[Depanshu],
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ECCV22(XXIV:252-267).
Springer DOI
2211
WWW Link. Use label hierarchy to reduce errors.
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Zheng, W.Q.[Wen-Qing],
Xie, J.[Jiyang],
Sun, X.[Xian],
Ma, Z.Y.[Zhan-Yu],
Structured Dropconnect for Uncertainty Inference in Image
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ICIP22(366-370)
IEEE DOI
2211
Deep learning, Uncertainty, Neural networks, Predictive models,
Entropy, Reliability, Uncertainty inference, image classification,
Dirichlet distribution
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Qu, H.X.[Hao-Xuan],
Li, Y.C.[Yan-Chao],
Foo, L.G.[Lin Geng],
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Improving the Reliability for Confidence Estimation,
ECCV22(XXVII:391-408).
Springer DOI
2211
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Corneanu, C.A.,
Escalera, S.,
Martinez, A.M.,
Computing the Testing Error Without a Testing Set,
CVPR20(2674-2682)
IEEE DOI
2008
Testing, Training, Topology, Cavity resonators, Measurement,
Network topology
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Humayoo, M.[Mahammad],
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Model-free Knockoffs for SLOPE-Adaptive Variable Selection with
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ICPR18(302-307)
IEEE DOI
1812
Computational modeling, Covariance matrices,
Hidden Markov models, Sparse matrices, Adaptation models,
Input variables
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Understanding random guessing line in ROC curve,
ICIVC17(1156-1159)
IEEE DOI
1708
Medical tests, ROC curve, interpretation, random guessing line
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Sunar, F.,
Piragnolo, M.,
Benchmark Of Machine Learning Methods For Classification Of A
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ISPRS16(B7: 335-340).
DOI Link
1610
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Robie, A.[Alice],
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Understanding classifier errors by examining influential neighbors,
CVPR15(3917-3925)
IEEE DOI
1510
BibRef
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ICPR10(3121-3124).
IEEE DOI
1008
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The Binormal Assumption on Precision-Recall Curves,
ICPR10(4263-4266).
IEEE DOI
1008
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ICPR08(1-4).
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0812
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Krishna, P.R.[P. Radha],
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An Unbalanced Data Classification Model Using Hybrid Sampling Technique
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PReMI07(341-348).
Springer DOI
0712
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Fisher, R.B.,
An Empirical Model for Saturation and Capacity in Classifier Spaces,
ICPR06(IV: 189-193).
IEEE DOI
0609
Determine the achievable classification rate for a database given
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Maloof, M.A.,
On machine learning, ROC analysis, and statistical tests of
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ICPR02(II: 204-207).
IEEE DOI
0211
BibRef
Johnson, A.Y.,
Bobick, A.F.,
Relationship between identification metrics:
Expected confusion and area under a ROC curve,
ICPR02(III: 662-666).
IEEE DOI
0211
BibRef
Rees, G.S.,
Wright, W.A.,
Greenway, P.,
ROC Method for the Evaluation of Multi-class
Segmentation/Classification Algorithms with Infrared Imagery,
BMVC02(Poster Session).
0208
BibRef
Ménard, M.,
Doget, T.,
Shahin, A.,
Ambiguity Concept and Switching Regression Models,
SCIA99(Pattern Recognition).
BibRef
9900
Kanungo, T.,
Gay, D.M.,
Haralick, R.M.,
Constrained monotone regression of ROC curves and histograms using
splines and polynomials,
ICIP95(II: 292-295).
IEEE DOI
9510
BibRef
Grossman, T.,
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Noise sensitivity signatures for model selection,
ICPR94(B:213-218).
IEEE DOI
9410
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Multiple Classifiers, Combining Classifiers, Combinations .