14.1.14.1 Error Estimation, Classification Accuracy

Chapter Contents (Back)
Evaluation, Classifiers. Error Estimation. ROC Analysis. 0511

Pearl, J.[Judea],
Capacity and Error Estimates for Boolean Classifiers with Limited Complexity,
PAMI(1), No. 4, October 1979, 350-356. BibRef 7910

Pearl, J.[Judea],
An application of rate-distortion theory to pattern recognition and classification,
PR(8), No. 1, January 1976, pp. 11-22.
Elsevier DOI 0309
BibRef

McLachlan, G.J.,
A note on the choice of a weighting function to give an efficient method for estimating the probability of misclassification,
PR(9), No. 3, October 1977, pp. 147-149.
Elsevier DOI 0309
BibRef

Bock, H.H.,
On some significance tests in cluster analysis,
Classification(2), 1985, pp. 77-108.
Springer DOI BibRef 8500

Lawoko, C.R.O., McLachlan, G.J.,
Asymptotic error rates of the W and Z statistics when the training observations are dependent,
PR(19), No. 6, 1986, pp. 467-471.
Elsevier DOI 0309
BibRef

Ganesalingam, S., McLachlan, G.J.,
Error rate estimation on the basis of posterior probabilities,
PR(12), No. 6, 1980, pp. 405-413.
Elsevier DOI 0309
BibRef

Chittineni, C.B.,
On the estimation of probability of error,
PR(9), No. 4, 1977, pp. 191-196.
Elsevier DOI 0309
BibRef

Chittineni, C.B.,
Estimation of probabilities of label imperfections and correction of mislabels,
PR(13), No. 3, 1981, pp. 257-268.
Elsevier DOI 0309
BibRef

van Otterloo, P.J., Young, I.T.,
A distribution-free geometric upper bound for the probability of error of a minimum distance classifier,
PR(10), No. 4, 1978, pp. 281-286.
Elsevier DOI 0309
BibRef

Glick, N.[Ned],
Additive estimators for probabilities of correct classification,
PR(10), No. 3, 1978, pp. 211-222.
Elsevier DOI 0309
BibRef

Engvall, J.L.[John L.],
A least upper bound for the average classification accuracy of multiple observers,
PR(12), No. 6, 1980, pp. 415-419.
Elsevier DOI 0309
BibRef

Kittler, J.V., Devijver, P.A.[Pierre A.],
An efficient estimator of pattern recognition system error probability,
PR(13), No. 3, 1981, pp. 245-249.
Elsevier DOI 0309
BibRef

Lahart, M.J.,
Estimation of Error Rates in Classification of Distorted Imagery,
PAMI(6), No. 4, July 1984, pp. 535-542. BibRef 8407

Fukunaga, K., and Flick, T.E.,
Classification Error for a Very Large Number of Classes,
PAMI(6), No. 6, November 1984, pp. 779-788.
See also Optimal Global Nearest Neighbor Metric, An. BibRef 8411

Fukunaga, K., and Hayes, R.R.,
Estimation of Classifier Performance,
PAMI(11), No. 10, October 1989, pp. 1087-1101.
IEEE DOI BibRef 8910

Pawlak, M.[Miroslaw],
On the asymptotic properties of smoothed estimators of the classification error rate,
PR(21), No. 5, 1988, pp. 515-524.
Elsevier DOI 0309
BibRef

Pawlak, M., Liao, X.,
Estimation of error rates using smoothed estimators,
ICPR88(II: 954-956).
IEEE DOI 8811
BibRef

Devroye, L.,
Automatic Pattern Recognition: A Study of the Probability of Error,
PAMI(10), No. 4, July 1988, pp. 530-543.
IEEE DOI BibRef 8807

Devroye, L., Gyorfi, L., Lugosi, G.,
Probabilistic Theory of Pattern Recognition,
Springer-Verlag1996. BibRef 9600

Zhu, Q.M.[Qiu-Ming],
On the minimum probability of error of classification with incomplete patterns,
PR(23), No. 11, 1990, pp. 1281-1290.
Elsevier DOI 0401
BibRef

Kalkanis, G., Conroy, G.V.,
Interval Error Estimators in Class Probability Trees,
PRL(17), No. 7, June 10 1996, pp. 705-712. 9607
BibRef

Durso, G., Menenti, M.,
Performance Indicators for the Statistical Evaluation of Digital Image Classifications,
PandRS(51), No. 2, April 1996, pp. 78-90. 9605
BibRef

Pal, N.R., Biswas, J.,
Cluster Validation Using Graph-Theoretic Concepts,
PR(30), No. 6, June 1997, pp. 847-857.
Elsevier DOI 9706
BibRef

Kloditz, C., Vanboxtel, A., Carfagna, E., Vandeursen, W.,
Estimating the Accuracy of Coarse Scale Classification Using High Scale Information,
PhEngRS(64), No. 2, February 1998, pp. 127-133. 9803
BibRef

Bax, E.,
Validation of Average Error Rate over Classifiers,
PRL(19), No. 2, February 1998, pp. 127-132. 9808
BibRef

Bax, E.[Eric],
Improved Hoeffding-style performance guarantees for accurate classifiers,
PRL(20), No. 4, April 1999, pp. 445-449. BibRef 9904

Bouchaffra, D.[Djamel], Govindaraju, V.[Venu], Srihari, S.[Sargur],
A Methodology for Mapping Scores to Probabilities,
PAMI(21), No. 9, September 1999, pp. 923-927.
IEEE DOI BibRef 9909
Earlier:
A Methodology for Deriving Probabilistic Correctness Measures from Recognizers,
CVPR98(930-935).
IEEE DOI Derive a probability of correctness that can be compared across all classifiers. BibRef

Ho, T.K., Basu, M.,
Complexity Measures of Supervised Classification Problems,
PAMI(24), No. 3, March 2002, pp. 289-300.
IEEE DOI 0202
BibRef
Earlier:
Measuring the Complexity of Classification Problems,
ICPR00(Vol II: 43-47).
IEEE DOI 0009
BibRef

Ho, T.K.[Tin Kam],
Data Complexity Analysis: Linkage between Context and Solution in Classification,
SSPR08(986-995).
Springer DOI 0812
BibRef
And: SSPR08(1).
Springer DOI 0812
BibRef

Clarkson, E.[Eric],
Bounds on the area under the receiver operating characteristic curve for the ideal observer,
JOSA-A(19), No. 10, October 2002, pp. 1963-1968.
WWW Link. 0210
BibRef

Clarkson, E.[Eric],
Estimation receiver operating characteristic curve and ideal observers for combined detection/estimation tasks,
JOSA-A(24), No. 12, December 2007, pp. B91-B98.
WWW Link. 0801
BibRef

Berikov, V.B.[Vladimir B.], Litvinenko, A.[Alexander],
The influence of prior knowledge on the expected performance of a classifier,
PRL(24), No. 15, November 2003, pp. 2537-2548.
Elsevier DOI 0308

See also approach to the evaluation of the performance of a discrete classifier, An. BibRef

Dougherty, E.R.[Edward R.], Brun, M.[Marcel],
A probabilistic theory of clustering,
PR(37), No. 5, May 2004, pp. 917-925.
Elsevier DOI 0405
BibRef

Braga-Neto, U.M.[Ulisses M.], Dougherty, E.R.[Edward R.],
Bolstered error estimation,
PR(37), No. 6, June 2004, pp. 1267-1281.
Elsevier DOI 0405
For further info:
WWW Link. BibRef

Braga-Neto, U.M.[Ulisses M.], Dougherty, E.R.[Edward R.],
Exact performance of error estimators for discrete classifiers,
PR(38), No. 11, November 2005, pp. 1799-1814.
Elsevier DOI 0509
BibRef

Zollanvari, A.[Amin], Braga-Neto, U.M.[Ulisses M.], Dougherty, E.R.[Edward R.],
On the sampling distribution of resubstitution and leave-one-out error estimators for linear classifiers,
PR(42), No. 11, November 2009, pp. 2705-2723.
Elsevier DOI 0907
Error estimation; Parametric classification; Linear discriminant analysis; Sampling distribution; Resubstitution; Leave-one-out BibRef

Zollanvari, A.[Amin], Dougherty, E.R.[Edward R.],
Moments and root-mean-square error of the Bayesian MMSE estimator of classification error in the Gaussian model,
PR(47), No. 6, 2014, pp. 2178-2192.
Elsevier DOI 1403
Linear discriminant analysis BibRef

Zollanvari, A.[Amin],
Nonoptimality of the Maximum-Weight Dependence Tree in Classification,
SPLetters(24), No. 1, January 2017, pp. 71-75.
IEEE DOI 1702
approximation theory BibRef

Braga-Neto, U.M.[Ulisses M.], Dougherty, E.R.[Edward R.],
Exact correlation between actual and estimated errors in discrete classification,
PRL(31), No. 5, 1 April 2010, pp. 407-412.
Elsevier DOI 1002
Error estimation; Discrete histogram rule; Correlation coefficient; Resubstitution; Leave-one-out; Cross-validation BibRef

Brun, M.[Marcel], Sima, C.[Chao], Hua, J.P.[Jian-Ping], Lowey, J.[James], Carroll, B.[Brent], Suh, E.[Edward], Dougherty, E.R.[Edward R.],
Model-based evaluation of clustering validation measures,
PR(40), No. 3, March 2007, pp. 807-824.
Elsevier DOI 0611
Clustering algorithms; Clustering errors; Validation indices BibRef

Edwards, D.C., Metz, C.E., Kupinski, M.A.,
Ideal Observers and Optimal ROC Hypersurfaces in N-Class Classification,
MedImg(23), No. 7, July 2004, pp. 891-895.
IEEE Abstract. 0407

See also Ideal observer approximation using bayesian classification neural networks. BibRef

Edwards, D.C., Metz, C.E., Nishikawa, R.M.,
The Hypervolume Under the ROC Hypersurface of 'Near-Guessing' and 'Near-Perfect' Observers in N-Class Classification Tasks,
MedImg(24), No. 3, March 2005, pp. 293-299.
IEEE Abstract. 0501
BibRef

Edwards, D.C., Metz, C.E.,
Restrictions on the three-class ideal observer's decision boundary lines,
MedImg(24), No. 12, December 2005, pp. 1566-1573.
IEEE DOI 0601
BibRef

Edwards, D.C., Metz, C.E.,
Optimization of Restricted ROC Surfaces in Three-Class Classification Tasks,
MedImg(26), No. 10, October 2007, pp. 1345-1356.
IEEE DOI 0711
BibRef

He, X., Metz, C.E., Tsui, B.M.W., Links, J.M., Frey, E.C.,
Three-Class ROC Analysis: A Decision Theoretic Approach Under the Ideal Observer Framework,
MedImg(25), No. 5, May 2006, pp. 571-581.
IEEE DOI 0605
BibRef

He, X., Frey, E.C.,
Three-Class ROC Analysis: The Equal Error Utility Assumption and the Optimality of Three-Class ROC Surface Using the Ideal Observer,
MedImg(25), No. 8, August 2006, pp. 979-986.
IEEE DOI 0608
BibRef

He, X.[Xin], Frey, E.C.,
The Meaning and Use of the Volume Under a Three-Class ROC Surface (VUS),
MedImg(27), No. 5, May 2008, pp. 577-588.
IEEE DOI 0711
BibRef

He, X., Frey, E.C.,
The Validity of Three-Class Hotelling Trace (3-HT) in Describing Three-Class Task Performance: Comparison of Three-Class Volume Under ROC Surface (VUS) and 3-HT,
MedImg(28), No. 2, February 2009, pp. 185-193.
IEEE DOI 0902
BibRef

He, X., Gallas, B.D., Frey, E.C.,
Three-Class ROC Analysis: Toward a General Decision Theoretic Solution,
MedImg(29), No. 1, January 2010, pp. 206-215.
IEEE DOI 1001
BibRef

Gallas, B.D.[Brandon D.], Pennello, G.A.[Gene A.], Myers, K.J.[Kyle J.],
Multireader multicase variance analysis for binary data,
JOSA-A(24), No. 12, December 2007, pp. B70-B80.
WWW Link. 0801
Analyzing ROC (receiver operating characteristic) curve data. BibRef

Park, S., Badano, A., Gallas, B.D., Myers, K.J.,
Incorporating Human Contrast Sensitivity in Model Observers for Detection Tasks,
MedImg(28), No. 3, March 2009, pp. 339-347.
IEEE DOI 0903
BibRef

Chen, W.J.[Wei-Jie], Gallas, B.D.[Brandon D.], Yousef, W.A.[Waleed A.],
Classifier variability: Accounting for training and testing,
PR(45), No. 7, July 2012, pp. 2661-2671.
Elsevier DOI 1203
Classifier evaluation; Training variability; Classifier stability; U-statistics; AUC BibRef

He, X., Frey, E.C.,
An Optimal Three-Class Linear Observer Derived From Decision Theory,
MedImg(26), No. 1, January 2007, pp. 77-83.
IEEE DOI 0701
BibRef

He, X.[Xin], Caffo, B.S., Frey, E.C.,
Toward Realistic and Practical Ideal Observer (IO) Estimation for the Optimization of Medical Imaging Systems,
MedImg(27), No. 10, October 2008, pp. 1535-1543.
IEEE DOI 0810
BibRef

He, X., Song, X., Frey, E.C.,
Application of Three-Class ROC Analysis to Task-Based Image Quality Assessment of Simultaneous Dual-Isotope Myocardial Perfusion SPECT (MPS),
MedImg(27), No. 11, November 2008, pp. 1556-1567.
IEEE DOI 0811
BibRef

Baraldi, A., Bruzzone, L., Blonda, P.,
Quality Assessment of Classification and Cluster Maps Without Ground Truth Knowledge,
GeoRS(43), No. 4, April 2005, pp. 857-873.
IEEE Abstract. 0501
BibRef

Baraldi, A.[Andrea], Humber, M.[Michael], Boschetti, L.[Luigi],
Quality Assessment of Pre-Classification Maps Generated from Spaceborne/Airborne Multi-Spectral Images by the Satellite Image Automatic Mapper™ and Atmospheric/Topographic Correction™-Spectral Classification Software Products: Part 2: Experimental Results,
RS(5), No. 10, 2013, pp. 5209-5264.
DOI Link 1311
BibRef

Santos-Pereira, C.M.[Carla M.], Pires, A.M.[Ana M.],
On optimal reject rules and ROC curves,
PRL(26), No. 7, 15 May 2005, pp. 943-952.
Elsevier DOI 0506
BibRef

Khurd, P., Gindi, G.,
Decision strategies that maximize the area under the LROC curve,
MedImg(24), No. 12, December 2005, pp. 1626-1636.
IEEE DOI 0601
BibRef

Khurd, P., Liu, B., Gindi, G.,
Ideal AFROC and FROC Observers,
MedImg(29), No. 2, February 2010, pp. 375-386.
IEEE DOI 1002
BibRef

Ahlqvist, O.[Ola], Gahegan, M.[Mark],
Probing the Relationship Between Classification Error and Class Similarity,
PhEngRS(71), No. 12, December 2005, pp. 1365-1374.
WWW Link. 0602
A method that predicts land-cover classification errors by using semantic similarity metrics derived from land-cover taxonomy definitions. BibRef

Landgrebe, T.C.W.[Thomas C.W.], Tax, D.M.J.[David M.J.], Paclík, P.[Pavel], Duin, R.P.W.[Robert P.W.],
The interaction between classification and reject performance for distance-based reject-option classifiers,
PRL(27), No. 8, June 2006, pp. 908-917.
Elsevier DOI Unseen classes; Reject-option; Model selection 0605
BibRef

Landgrebe, T.C.W.[Thomas C.W.], Paclik, P.[Pavel],
The ROC skeleton for multiclass ROC estimation,
PRL(31), No. 9, 1 July 2010, pp. 949-958.
Elsevier DOI 1004
ROC analysis; Operating characteristics; Multiclass ROC; Cost sensitive optimisation
See also Precision-recall operating characteristic (P-ROC) curves in imprecise environments. BibRef

Paclik, P.[Pavel], Lai, C.[Carmen], Novovicova, J.[Jana], Duin, R.P.W.[Robert P.W.],
Variance estimation for two-class and multi-class ROC analysis using operating point averaging,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Paclik, P.[Pavel], Lai, C.[Carmen], Landgrebe, T.C.W.[Thomas C.W.], Duin, R.P.W.[Robert P.W.],
ROC Analysis and Cost-Sensitive Optimization for Hierarchical Classifiers,
ICPR10(2977-2980).
IEEE DOI 1008
BibRef

Fawcett, T.[Tom],
ROC graphs with instance-varying costs,
PRL(27), No. 8, June 2006, pp. 882-891.
Elsevier DOI Cost-sensitive learning; Classifier evaluation 0605
BibRef

Everson, R.M.[Richard M.], Fieldsend, J.E.[Jonathan E.],
Multi-class ROC analysis from a multi-objective optimisation perspective,
PRL(27), No. 8, June 2006, pp. 918-927.
Elsevier DOI Evolutionary computation; Pareto optimality; Gini coefficient 0605
BibRef

Matei, B.C.[Bogdan C.], Meer, P.[Peter],
Estimation of Nonlinear Errors-in-Variables Models for Computer Vision Applications,
PAMI(28), No. 10, October 2006, pp. 1537-1552.
IEEE DOI 0609
BibRef
Earlier:
A General Method for Errors-in-Variables Problems in Computer Vision,
CVPR00(II: 18-25).
IEEE DOI 0005
HEIV. All measurements are noisy. Related to Sampson, renormalization, numerical.
See also HEIV based estimation. BibRef

Georgescu, B.[Bogdan],
HEIV based estimation,
OnlineSeptember, 2002. Code, HEIV.
WWW Link. Code related to above paper.
See also Estimation of Nonlinear Errors-in-Variables Models for Computer Vision Applications. BibRef 0209

Landgrebe, T.C.W.[Thomas C.W.], Duin, R.P.W.[Robert P.W.],
Approximating the multiclass ROC by pairwise analysis,
PRL(28), No. 13, 1 October 2007, pp. 1747-1758.
Elsevier DOI 0709
ROC analysis; Multiclass ROC; Cost sensitive; Threshold optimisation BibRef

Waegeman, W.[Willem], de Baets, B.[Bernard], Boullart, L.[Luc],
ROC analysis in ordinal regression learning,
PRL(29), No. 1, 1 January 2008, pp. 1-9.
Elsevier DOI 0711
ROC analysis; Ranking; Ordinal regression; Unbalanced learning problems; Performance measures; Machine learning BibRef

Marrocco, C.[Claudio], Duin, R.P.W., Tortorella, F.[Francesco],
Maximizing the area under the ROC curve by pairwise feature combination,
PR(41), No. 6, June 2008, pp. 1961-1974.
Elsevier DOI 0802
Two-class problems; ROC curve; Ranking; AUC BibRef

Chen, D.M.[Dong Mei], Wei, H.[Hui],
The effect of spatial autocorrelation and class proportion on the accuracy measures from different sampling designs,
PandRS(64), No. 2, March 2009, pp. 140-150.
Elsevier DOI 0903
Accuracy assessment; Classification error; Sampling; Spatial autocorrelation; Class proportion BibRef

Chatelain, C.[Clement], Adam, S.[Sebastien], Lecourtier, Y.[Yves], Heutte, L.[Laurent], Paquet, T.[Thierry],
A multi-model selection framework for unknown and/or evolutive misclassification cost problems,
PR(43), No. 3, March 2010, pp. 815-823.
Elsevier DOI 1001
ROC front; Multi-model selection; Multi-objective optimization; ROC curve; Handwritten digit/outlier discrimination BibRef

Ooms, D., Palm, R., Leemans, V., Destain, M.F.,
A sorting optimization curve with quality and yield requirements,
PRL(31), No. 9, 1 July 2010, pp. 983-990.
Elsevier DOI 1004
Binary classification; Classifier optimization; ROC; SOC; Sorting; Threshold BibRef

Schubert, C.M.[Christine M.], Thorsen, S.N.[Steven N.], Oxley, M.E.[Mark E.],
The ROC manifold for classification systems,
PR(44), No. 2, February 2011, pp. 350-362.
Elsevier DOI 1011
Classification; Multiple classes; Receiver operating characteristic (ROC) curve; ROC manifold; Bayes cost BibRef

Wunderlich, A., Noo, F.,
A Nonparametric Procedure for Comparing the Areas Under Correlated LROC Curves,
MedImg(31), No. 11, November 2012, pp. 2050-2061.
IEEE DOI 1211
BibRef

Rodríguez, J.D.[Juan D.], Pérez, A.[Aritz], Lozano, J.A.[Jose A.],
A general framework for the statistical analysis of the sources of variance for classification error estimators,
PR(46), No. 3, March 2013, pp. 855-864.
Elsevier DOI 1212
Supervised classification; Error estimation; Prediction error; Sensitivity analysis; Sources of variance; Model selection BibRef

Atashpaz-Gargari, E.[Esmaeil], Sima, C.[Chao], Braga-Neto, U.M.[Ulisses M.], Dougherty, E.R.[Edward R.],
Relationship between the accuracy of classifier error estimation and complexity of decision boundary,
PR(46), No. 5, May 2013, pp. 1315-1322.
Elsevier DOI 1302
Error estimation; Distribution complexity; Small samples; Complexity of decision boundary BibRef

Hand, D.J., Anagnostopoulos, C.,
When is the area under the receiver operating characteristic curve an appropriate measure of classifier performance?,
PRL(34), No. 5, 1 April 2013, pp. 492-495.
Elsevier DOI 1303
Area under the curve; Classification; Gini coefficient; ROC curve; Screening BibRef

Hand, D.J., Anagnostopoulos, C.,
A better Beta for the H measure of classification performance,
PRL(40), No. 1, 2014, pp. 41-46.
Elsevier DOI 1403
Supervised classification BibRef

Hernández-Orallo, J.[José],
ROC curves for regression,
PR(46), No. 12, 2013, pp. 3395-3411.
Elsevier DOI 1308
ROC Curves BibRef

Mas, J.F.[Jean-François], Filho, B.S.[Britaldo Soares], Pontius, R.G.[Robert Gilmore], Gutiérrez, M.F.[Michelle Farfán], Rodrigues, H.[Hermann],
A Suite of Tools for ROC Analysis of Spatial Models,
IJGI(2), No. 3, 2013, pp. 869-887.
DOI Link 1310
BibRef

Bradley, A.P.[Andrew P.],
Half-AUC for the evaluation of sensitive or specific classifiers,
PRL(38), No. 1, 2014, pp. 93-98.
Elsevier DOI 1402
ROC curves BibRef

Clémençon, S.[Stéphan], Robbiano, S.[Sylvain],
Building confidence regions for the ROC surface,
PRL(46), No. 1, 2014, pp. 67-74.
Elsevier DOI 1407
Asymptotic accuracy BibRef

Sun, X.[Xu], Xu, W.C.[Wei-Chao],
Fast Implementation of DeLong's Algorithm for Comparing the Areas Under Correlated Receiver Operating Characteristic Curves,
SPLetters(21), No. 11, November 2014, pp. 1389-1393.
IEEE DOI 1408
Monte Carlo methods BibRef

Wen, Z., Hu, Y., Zhu, W.,
A Novel Classification Method of Halftone Image via Statistics Matrices,
IP(23), No. 11, November 2014, pp. 4724-4736.
IEEE DOI 1410
Error analysis BibRef

Wang, S.J.[Shi-Jun], Li, D.[Diana], Petrick, N.[Nicholas], Sahiner, B.[Berkman], Linguraru, M.G.[Marius George], Summers, R.M.[Ronald M.],
Optimizing area under the ROC curve using semi-supervised learning,
PR(48), No. 1, 2015, pp. 276-287.
Elsevier DOI 1410
Receiver operating characteristic BibRef

Bernard, S.[Simon], Chatelain, C.[Clément], Adam, S.[Sébastien], Sabourin, R.[Robert],
The Multiclass ROC Front method for cost-sensitive classification,
PR(52), No. 1, 2016, pp. 46-60.
Elsevier DOI 1601
Multiclass classification BibRef

Dubos, C., Bernard, S.[Simon], Adam, S.[Sébastien], Sabourin, R.[Robert],
ROC-based cost-sensitive classification with a reject option,
ICPR16(3320-3325)
IEEE DOI 1705
Focusing, Medical diagnosis, Optimization, Pathology, Protocols, Support vector machines, BibRef

Wu, Y.J.[Yun-Jhong], Chiang, C.T.[Chin-Tsang],
ROC representation for the discriminability of multi-classification markers,
PR(60), No. 1, 2016, pp. 770-777.
Elsevier DOI 1609
Discriminability BibRef

Wunderlich, A.[Adam], Goossens, B.[Bart], Abbey, C.K.[Craig K.],
Optimal Joint Detection and Estimation That Maximizes ROC-Type Curves,
MedImg(35), No. 9, September 2016, pp. 2164-2173.
IEEE DOI 1609
Biomedical imaging BibRef

Cook, J.A.[Jonathan Aaron],
ROC curves and nonrandom data,
PRL(85), No. 1, 2017, pp. 35-41.
Elsevier DOI 1612
ROC curves BibRef

Wang, Z.[Ziyin], Farhand, S.[Sepehr], Tsechpenakis, G.[Gavriil],
Fading Affect Bias: Improving the Trade-Off Between Accuracy and Efficiency in Feature Clustering,
MVA(30), No. 2, March 2019, pp. 255-268.
WWW Link. 1904
BibRef
Earlier: WACV18(775-783)
IEEE DOI 1806
data structures, pattern clustering, stochastic processes, clustering algorithms, Kernel BibRef

Omar, L.[Luma], Ivrissimtzis, I.[Ioannis],
Using theoretical ROC curves for analysing machine learning binary classifiers,
PRL(128), 2019, pp. 447-451.
Elsevier DOI 1912
Binary classification, Classifier analysis, Detection theory, ROC curve, Beta distribution BibRef

Lai, H., Xu, W.,
Statistical Properties of Kendall's Tau Under Contaminated Gaussian Model With Applications in Random Signal Detection,
SPLetters(27), 2020, pp. 655-659.
IEEE DOI 2005
Signal detection, Correlation, Mathematical model, Robustness, Receivers, Random variables, Kendall's tau (KT), receiver operating characteristic (ROC) curve BibRef

Parmigiani, G.[Giovanni],
Receiver operating characteristic curves with an indeterminacy zone,
PRL(136), 2020, pp. 94-100.
Elsevier DOI 2008
Receiver operating characteristic (ROC), Indeterminacy in classification BibRef

Song, M., Shang, X., Chang, C.I.,
3-D Receiver Operating Characteristic Analysis for Hyperspectral Image Classification,
GeoRS(58), No. 11, November 2020, pp. 8093-8115.
IEEE DOI 2011
Hyperspectral imaging, Support vector machines, Detectors, Receivers, Feature extraction, iterative linearly constrained minimum variance (ILCMV) BibRef

Chang, C.I.[Chein-I],
An Effective Evaluation Tool for Hyperspectral Target Detection: 3D Receiver Operating Characteristic Curve Analysis,
GeoRS(59), No. 6, June 2021, pp. 5131-5153.
IEEE DOI 2106
Detectors, Tools, Hyperspectral imaging, Receivers, Probability, target detection in BKG (TD-BS) BibRef

Rachakonda, A.R.[Aditya Ramana], Bhatnagar, A.[Ayush],
ARatio: Extending area under the ROC curve for probabilistic labels,
PRL(150), 2021, pp. 265-271.
Elsevier DOI 2109
AUC ROC, ARatio, Metrics, Probabilistic labels, Confusion matrix BibRef

Dang, Z.Y.[Zhi-Yuan], Li, X.[Xiang], Gu, B.[Bin], Deng, C.[Cheng], Huang, H.[Heng],
Large-Scale Nonlinear AUC Maximization via Triply Stochastic Gradients,
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, kernel methods BibRef

Yu, X.Y.[Xiao-Yu], Chen, Y.L.[Ying-Lu], Zhou, G.F.[Guo-Fu], Liu, Y.[Yan], Li, F.C.[Fu-Chao], Wang, Z.F.[Zhi-Fei],
Error Refactor loss based on error analysis in image classification,
IET-CV(16), No. 2, 2022, pp. 192-203.
DOI Link 2202
ER-loss, error analysis, image classification, similar features, softmax BibRef

Li, K.[Kaiyan], Zhou, W.M.[Wei-Min], Li, H.[Hua], Anastasio, M.A.[Mark A.],
A Hybrid Approach for Approximating the Ideal Observer for Joint Signal Detection and Estimation Tasks by Use of Supervised Learning and Markov-Chain Monte Carlo Methods,
MedImg(41), No. 5, May 2022, pp. 1114-1124.
IEEE DOI 2205
Task analysis, Observers, Estimation, Monte Carlo methods, Training, Signal detection, Multitasking, Numerical observers, deep learning BibRef

Yang, Z.Y.[Zhi-Yong], Xu, Q.Q.[Qian-Qian], Bao, S.[Shilong], Cao, X.C.[Xiao-Chun], Huang, Q.M.[Qing-Ming],
Learning With Multiclass AUC: Theory and Algorithms,
PAMI(44), No. 11, November 2022, pp. 7747-7763.
IEEE DOI 2210
Area under the ROC. Measurement, Machine learning, Complexity theory, Stochastic processes, Risk management, Upper bound, machine learning BibRef

Carrington, A.M.[André M.], Manuel, D.G.[Douglas G.], Fieguth, P.W.[Paul W.], Ramsay, T.[Tim], Osmani, V.[Venet], Wernly, B.[Bernhard], Bennett, C.[Carol], Hawken, S.[Steven], Magwood, O.[Olivia], Sheikh, Y.[Yusuf], McInnes, M.[Matthew], Holzinger, A.[Andreas],
Deep ROC Analysis and AUC as Balanced Average Accuracy, for Improved Classifier Selection, Audit and Explanation,
PAMI(45), No. 1, January 2023, pp. 329-341.
IEEE DOI 2212
Sensitivity, Area measurement, Hospitals, Predictive models, Analytical models, Measurement uncertainty, Licenses, audit BibRef

Yang, Z.Y.[Zhi-Yong], Xu, Q.Q.[Qian-Qian], Bao, S.[Shilong], He, Y.[Yuan], Cao, X.C.[Xiao-Chun], Huang, Q.M.[Qing-Ming],
Optimizing Two-Way Partial AUC With an End-to-End Framework,
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 BibRef

Yang, Z.Y.[Zhi-Yong], Xu, Q.Q.[Qian-Qian], Hou, W.Z.[Wen-Zheng], Bao, S.L.[Shi-Long], He, Y.[Yuan], Cao, X.C.[Xiao-Chun], Huang, Q.M.[Qing-Ming],
Revisiting AUC-Oriented Adversarial Training With Loss-Agnostic Perturbations,
PAMI(45), No. 12, December 2023, pp. 15494-15511.
IEEE DOI 2311
Area Under the ROC curve. BibRef

Xu, J.Y.[Jing-Yan],
On the bias in the AUC variance estimate,
PRL(178), 2024, pp. 62-68.
Elsevier DOI 2402
Binary classification receiver operating characteristic (ROC), ANOVA BibRef


Kienitz, D.[Daniel], Komendantskaya, E.[Ekaterina], Lones, M.[Michael],
Comparing Complexities of Decision Boundaries for Robust Training: A Universal Approach,
ACCV22(VI:627-645).
Springer DOI 2307
BibRef

Adeodato, P.[Paulo], Melo, S.[Sílvio],
Kolmogorov-Smirnov and ROC curve metrics for binary classification performance assessment are equivalent,
ICPR22(1194-1199)
IEEE DOI 2212
Measurement, Decision support systems, Power measurement, Decision making, Key performance indicator, Space transformation matrix BibRef

Garg, A.[Ashima], Sani, D.[Depanshu], Anand, S.[Saket],
Learning Hierarchy Aware Features for Reducing Mistake Severity,
ECCV22(XXIV:252-267).
Springer DOI 2211

WWW Link. Use label hierarchy to reduce errors. BibRef

Zheng, W.Q.[Wen-Qing], Xie, J.[Jiyang], Sun, X.[Xian], Ma, Z.Y.[Zhan-Yu],
Structured Dropconnect for Uncertainty Inference in Image Classification,
ICIP22(366-370)
IEEE DOI 2211
Deep learning, Uncertainty, Neural networks, Predictive models, Entropy, Reliability, Uncertainty inference, image classification, Dirichlet distribution BibRef

Qu, H.X.[Hao-Xuan], Li, Y.C.[Yan-Chao], Foo, L.G.[Lin Geng], Kuen, J.[Jason], Gu, J.X.[Jiu-Xiang], Liu, J.[Jun],
Improving the Reliability for Confidence Estimation,
ECCV22(XXVII:391-408).
Springer DOI 2211
BibRef

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 BibRef

Humayoo, M.[Mahammad], Cheng, X.Q.[Xue-Qi],
Model-free Knockoffs for SLOPE-Adaptive Variable Selection with Controlled False Discovery Rate,
ICPR18(302-307)
IEEE DOI 1812
Computational modeling, Covariance matrices, Hidden Markov models, Sparse matrices, Adaptation models, Input variables BibRef

Zhu, D.D.[Dan-Dan], Cui, Y.[Yan],
Understanding random guessing line in ROC curve,
ICIVC17(1156-1159)
IEEE DOI 1708
Medical tests, ROC curve, interpretation, random guessing line BibRef

Pirotti, F., Sunar, F., Piragnolo, M.,
Benchmark Of Machine Learning Methods For Classification Of A Sentinel-2 Image,
ISPRS16(B7: 335-340).
DOI Link 1610
BibRef

Kabra, M.[Mayank], Robie, A.[Alice], Branson, K.[Kristin],
Understanding classifier errors by examining influential neighbors,
CVPR15(3917-3925)
IEEE DOI 1510
BibRef

Brodersen, K.H.[Kay Henning], Ong, C.S.[Cheng Soon], Stephan, K.E.[Klaas Enno], Buhmann, J.M.[Joachim M.],
The Balanced Accuracy and Its Posterior Distribution,
ICPR10(3121-3124).
IEEE DOI 1008
BibRef

Brodersen, K.H.[Kay Henning], Ong, C.S.[Cheng Soon], Stephan, K.E.[Klaas Enno], Buhmann, J.M.[Joachim M.],
The Binormal Assumption on Precision-Recall Curves,
ICPR10(4263-4266).
IEEE DOI 1008
BibRef

He, T.T.[Ting-Ting], Huo, Q.A.[Qi-Ang],
A study of a new misclassification measure for minimum classification error training of prototype-based pattern classifiers,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Padmaja, T.M.[T. Maruthi], Dhulipalla, N.[Narendra], Krishna, P.R.[P. Radha], Bapi, R.S.[Raju S.], Laha, A.,
An Unbalanced Data Classification Model Using Hybrid Sampling Technique for Fraud Detection,
PReMI07(341-348).
Springer DOI 0712
BibRef

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 a level of noise. BibRef

Maloof, M.A.,
On machine learning, ROC analysis, and statistical tests of significance,
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., Lapedes, A.,
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 .


Last update:Mar 16, 2024 at 20:36:19