|
| This Article | ||
| ||
| Share | ||
| Bibliographic References | ||
| Add to: | ||
| | ||
| Search | ||
| ||
| ASCII Text | x | ||
| Edith Grall-Maës, Pierre Beauseroy, "Optimal Decision Rule with Class-Selective Rejection and Performance Constraints," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 11, pp. 2073-2082, November, 2009. | |||
| BibTex | x | ||
| @article{ 10.1109/TPAMI.2008.239, author = {Edith Grall-Maës and Pierre Beauseroy}, title = {Optimal Decision Rule with Class-Selective Rejection and Performance Constraints}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {31}, number = {11}, issn = {0162-8828}, year = {2009}, pages = {2073-2082}, doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2008.239}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - JOUR JO - IEEE Transactions on Pattern Analysis and Machine Intelligence TI - Optimal Decision Rule with Class-Selective Rejection and Performance Constraints IS - 11 SN - 0162-8828 SP2073 EP2082 EPD - 2073-2082 A1 - Edith Grall-Maës, A1 - Pierre Beauseroy, PY - 2009 KW - Decision rule KW - pattern classification KW - multiclass KW - class-selective rejection KW - partial rejection KW - preselection KW - constraints KW - statistical decision theory. VL - 31 JA - IEEE Transactions on Pattern Analysis and Machine Intelligence ER - | |||
[1] J.-S. Lin, S.-C.B. Lo, A. Hasegawa, M.T. Freedman, and S.K. Mun, “Reduction of False Positives in Lung Nodule Detection Using a Two-Level Neural Classification,” IEEE Trans. Medical Imaging, vol. 15, no. 2, pp. 206-217, Apr. 1996.
[2] Y. Peng, Q. Huang, P. Jiang, and J. Jiang, “Cost-Sensitive Ensemble of Support Vector Machines for Effective Detection of Microcalcification in Breast Cancer Diagnosis,” Lecture Notes in Artificial Intelligence, vol. 3614, pp. 483-493, 2005.
[3] J. Neyman and E. Pearson, “On the Problem of the Most Efficient Tests of Statistical Hypothesis,” Philosophical Trans. Royal Soc. A, vol. 231, no. 9, pp. 289-337, 1933.
[4] K. Fukunaga, Introduction to Statistical Pattern Recognition. Academic Press, 1990.
[5] R.O. Duda and P.E. Hart, Pattern Classification and Scene Analysis. Wiley, 1973.
[6] C. Chow, “On Optimum Character Recognition System Using Decision Functions,” IRE Trans. Electronic Computers, vol. 6, pp.247-254, 1957.
[7] C. Chow, “On Optimum Recognition Error and Reject Tradeoff,” IEEE Trans. Information Theory, vol. 16, no. 1, pp. 41-46, Jan. 1970.
[8] G. Fumera, F. Roli, and G. Giacinto, “Reject Option with Multiple Thresholds,” Pattern Recognition, vol. 33, no. 12, pp. 2099-2101, 2000.
[9] A. Bounsiar, P. Beauseroy, and E. Grall-Maës, “A Straightforward SVM Approach for Classification with Constraint,” Proc. European Signal Processing Conf., 2005.
[10] E. Grall-Maës, P. Beauseroy, and A. Bounsiar, “Classification avec Contraintes: Problématique et Apprentissage d'une Règle de Décision,” Proc. GRETSI '05, pp. 1145-1148, 2005.
[11] C.M. Santos-Pereira and A. Pires, “On Optimal Reject Rules and roc Curves,” Pattern Recognition Letters, vol. 26, no. 7, pp. 943-952, 2005.
[12] S. Gupta, “On Some Multiple Decision (Selection and Ranking) Rules,” Technometrics, vol. 7, pp. 225-245, 1965.
[13] T. Ha, “On Functional Relation between Class-Selective Rejection Error and Average Number of Classes,” Proc. 1996 IEEE Int'l Joint Symp. Intelligence and Systems, pp. 282-287, 1996.
[14] T. Ha, “The Optimum Class-Selective Rejection Rule,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 6, pp. 608-615, June 1997.
[15] T. Ha, “Optimum Decision Rules in Pattern Recognition,” Proc. Joint IAPR Int'l Workshops Advances in Pattern Recognition, Structural and Syntactic Pattern Recognition, pp. 726-735, 1998.
[16] T. Horiuchi, “Class-Selective Rejection Rule to Minimize the Maximum Distance between Selected Classes,” Pattern Recognition, vol. 31, no. 10, pp. 579-1588, 1998.
[17] A. Bounsiar, P. Beauseroy, and E. Grall-Maës, “A Straightforward SVM Approach for Classification with Constraint,” Proc. European Signal Processing Conf., 2005.
[18] F.R. Bach, D. Heckerman, and E. Horvitz, “On the Path to an Ideal ROC Curve: Considering Cost Asymmetry in Learning Classifiers,” Proc. 10th Int'l Workshop Artificial Intelligence and Statistics, 2005.
[19] M. Davenport, R. Baraniuk, and C. Scott, “Controlling False Alarms with Support Vector Machines,” Proc. IEEE Int'l Conf. Acoustics, Speech, and Signal Processing, 2006.
[20] F. Tortorella, “Reducing the Classification Cost of Support Vector Classifiers through an roc-Based Reject Rule,” Pattern Analysis and Applications, vol. 7, pp. 128-143, 2004.
[21] J. Platt, “Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods,” Advances in Large Margin Classifiers, A.J. Smola, P.L. Bartlet, B. Schölkopf, and D. Schurmans, eds., pp. 61-74, MIT Press, 2000.
[22] G. Fumera and F. Roli, “Support Vector Machines with Embedded Rejection Option,” Pattern Recognition with Support Vector Machines, S. Lee and A. Verri, eds., pp. 68-82, Springer, 2002.
[23] M. Li and I.K. Sethi, “Confidence-Based Classifier Design,” Pattern Recognition, vol. 39, pp. 1230-1240, 2006.
[24] E. Grall, P. Beauseroy, and A. Bounsiar, “Multilabel Classification Rule with Performance Constraints,” Proc. IEEE Int'l Conf. Acoustics, Speech, and Signal Processing, 2006.
[25] B. Dubuisson and M. Masson, “A Statistical Decision Rule with Incomplete Knowledge about Classes,” Pattern Recognition, vol. 26, no. 1, pp. 155-165, 1993.
[26] J. Schurmann, Pattern Classification: A Unified View of Statistical and Neural Approaches. Wiley, 1996.
[27] R. Fletcher, Practical Methods of Optimization, second ed. Wiley, 1987.
[28] S.S. Rao, Engineering Optimization: Theory and Practice. Wiley, 1996.
[29] M. Minoux, Mathematical Programming: Theory and Algorithms. Wiley, 1986.
[30] M. Bazaraa, H. Sherali, and C. Shetty, Nonlinear Programming: Theory and Algorithms. Wiley, 1993.
[31] E. Grall, P. Beauseroy, and A. Bounsiar, “Quality Assessment of a Supervised Multilabel Classification Rule with Performance Constraints,” Proc. 14th European Signal Processing Conf., 2006.

