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.
WWW Version.
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.
WWW Version.
0309
BibRef
Bock, H.H.,
On some significance tests in cluster analysis,
Classification(2), 1985, pp. 77-108.
Springer DOI Link
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.
WWW Version.
0309
BibRef
Ganesalingam, S.,
McLachlan, G.J.,
Error rate estimation on the basis of posterior probabilities,
PR(12), No. 6, 1980, pp. 405-413.
WWW Version.
0309
BibRef
Chittineni, C.B.,
On the estimation of probability of error,
PR(9), No. 4, 1977, pp. 191-196.
WWW Version.
0309
BibRef
Chittineni, C.B.,
Estimation of probabilities of label imperfections and correction of
mislabels,
PR(13), No. 3, 1981, pp. 257-268.
WWW Version.
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.
WWW Version.
0309
BibRef
Glick, N.[Ned],
Additive estimators for probabilities of correct classification,
PR(10), No. 3, 1978, pp. 211-222.
WWW Version.
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.
WWW Version.
0309
BibRef
Kittler, J.V.,
Devijver, P.A.,
An efficient estimator of pattern recognition system error probability,
PR(13), No. 3, 1981, pp. 245-249.
WWW Version.
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 Abstract. IEEE Top Reference.
WWW Version.
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.
WWW Version.
0309
BibRef
Pawlak, M.,
Liao, X.,
Estimation of error rates using smoothed estimators,
ICPR88(II: 954-956).
IEEE DOI Link
8811
BibRef
Devroye, L.,
Automatic Pattern Recognition: A Study of the Probability of Error,
PAMI(10), No. 4, July 1988, pp. 530-543.
IEEE Abstract. IEEE Top Reference.
WWW Version.
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.
WWW Version.
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.
WWW Version.
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 Abstract. IEEE Top Reference.
WWW Version.
BibRef
9909
Earlier:
A Methodology for Deriving Probabilistic Correctness Measures
from Recognizers,
CVPR98(930-935).
IEEE Abstract. IEEE Top Reference. Derive a probability of correctness that can be compared across all
classifiers.
BibRef
Tulyakov, S.,
Govindaraju, V.,
Combining matching scores in identification model,
ICDAR05(II: 1151-1155).
IEEE DOI Link
0508
Combining scores. Best score not always best, depending on number of options.
BibRef
Ho, T.K.,
Basu, M.,
Complexity Measures of Supervised Classification Problems,
PAMI(24), No. 3, March 2002, pp. 289-300.
IEEE Abstract. IEEE Top Reference.
WWW Version.
0202
BibRef
Earlier:
Measuring the Complexity of Classification Problems,
ICPR00(Vol II: 43-47).
IEEE DOI Link
HTML Version.
0009
BibRef
Ho, T.K.[Tin Kam],
Data Complexity Analysis: Linkage between Context and Solution in
Classification,
SSPR08(986-995).
Springer DOI Link
0812
BibRef
And:
SSPR08(1).
Springer DOI Link
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 Version.
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 Version.
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.
WWW Version.
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.
WWW Version.
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.
WWW Version.
0405
For further info:
WWW Version.
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.
WWW Version.
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 Link
WWW Version.
0907
Error estimation; Parametric classification; Linear discriminant
analysis; Sampling distribution; Resubstitution; Leave-one-out
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.
WWW Version.
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. IEEE Top Reference.
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. IEEE Top Reference.
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 Link
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 Link
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 Link
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 Link
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 Link
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 Link
0902
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 Link
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 Link
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 Link
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. IEEE Top Reference.
0501
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.
WWW Version.
0506
BibRef
DeVore, M.D.,
Estimates of Error Probability for Complex Gaussian Channels with
Generalized Likelihood Ratio Detection,
PAMI(27), No. 10, October 2005, pp. 1580-1591.
IEEE DOI Link
0509
Two-class hypothesis testing.
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 Link
0601
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 Version.
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.
WWW Version. Unseen classes; Reject-option; Model selection
0605
BibRef
Fawcett, T.[Tom],
ROC graphs with instance-varying costs,
PRL(27), No. 8, June 2006, pp. 882-891.
WWW Version. 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.
WWW Version. 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 Link
0609
BibRef
Earlier:
A General Method for Errors-in-Variables Problems in Computer Vision,
CVPR00(II: 18-25).
IEEE Abstract. IEEE Top Reference.
WWW Version.
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 Version.
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.
WWW Version.
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.
WWW Version.
0711
ROC analysis; Ranking; Ordinal regression; Unbalanced learning problems;
Performance measures; Machine learning
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 Version.
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 Link
0903
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.
WWW Version.
0802
Two-class problems; ROC curve; Ranking; AUC
BibRef
El Ayadi, M.M.H.[Moataz M.H.],
Kamel, M.S.[Mohamed S.],
Karray, F.[Fakhri],
Toward a tight upper bound for the error probability of the binary
Gaussian classification problem,
PR(41), No. 6, June 2008, pp. 2120-2132.
WWW Version.
0802
Binary classification; Bayesian decision rule; Decision boundary;
Error probability; Monte-Carlo simulations; Multivariate normal
distribution; Quadratic surfaces
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 Link
WWW Version.
0903
Accuracy assessment; Classification error; Sampling; Spatial
autocorrelation; Class proportion
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 Link
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 Link
0712
BibRef
Fisher, R.B.,
An Empirical Model for Saturation and Capacity in Classifier Spaces,
ICPR06(IV: 189-193).
WWW Version.
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 Link
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 Link
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
Raudys, S.J.,
Diciunas, V.,
Expected Error of Minimum Empirical Error and
Maximal Margin Classifiers,
ICPR96(II: 875-879).
IEEE DOI Link
9608
(Institute of Mathematics and Informatics, LIT)
BibRef
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 Link
9510
BibRef
Grossman, T.,
Lapedes, A.,
Noise sensitivity signatures for model selection,
ICPR94(B:213-218).
IEEE DOI Link
9410
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Multiple Classifiers, Combining Classifiers, Combinations .