A boosting method for maximizing the partial area under the ROC curve

BMC Bioinformatics

Volumn 11, Issue , 2010, Pages

  Komori, Osamu ^a   Eguchi, Shinto ^a,b  

^a INSTITUTE OF STATISTICAL MATHEMATICS   (Japan)

^b GRADUATE UNIVERSITY FOR ADVANCED STUDIES   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY OF CLASSIFICATIONS; AREA UNDER THE ROC CURVE; DISCRIMINATION PROBLEM; FALSE POSITIVE RATES; FILTERING PROCEDURES; NATURAL CUBIC SPLINES; NONLINEAR COMBINATION; RECEIVER OPERATING CHARACTERISTIC CURVES;

DECISION TREES;

BIOLOGICAL MARKER;

ALGORITHM; AREA UNDER THE CURVE; ARTICLE; AUTOMATED PATTERN RECOGNITION; CHEMISTRY; METHODOLOGY; RECEIVER OPERATING CHARACTERISTIC;

ALGORITHMS; AREA UNDER CURVE; BIOLOGICAL MARKERS; PATTERN RECOGNITION, AUTOMATED; ROC CURVE;

EID: 77953243025     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-11-314     Document Type: Article

References (32)

1. Bamber D. The area above the ordinal dominance graph and the area below the receiver operating characteristic graph. Journal of Mathematical Psychology 1975, 12:387-415.
2. Pepe MS, Janes H, Longton G, Leisenring W, Newcomb P. Limitation of the odds ratio in gauging the performance of a diagnostic, prognostic, or screening marker. American Journal of Epidemiology 2004, 159:882-890. 10.1093/aje/kwh101, 15105181.
3. Cook NR. Use and misuse of the receiver operating characteristic curve in risk prediction. Circulation 2007, 115:928-935. 10.1161/CIRCULATIONAHA.106.672402, 17309939.
4. Pencina MJ, D'Agostino RB, D'Agostino RB, Vasan RS. Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond. Statistics in Medicine 2008, 27:157-172. 10.1002/sim.2929, 17569110.
5. Baker SG. The central role of receiver operating characteristic (ROC) curves in evaluating tests for the early detection of cancer. Journal of the National Cancer Institute 2003, 95:511-515. 10.1093/jnci/95.7.511, 12671018.
6. Qi Y, Joseph ZB, Seetharaman JK. Evaluation of different biological data and computational classification methods for use in protein interaction prediction. Proteins: Structure, Function, and Bioinformatics 2006, 63:490-500.
7. Hadjiiski L, Sahiner B, Chan HP, Petrick N, Helvie M. Classification of malignant and benign masses based on hybrid ART2LDA approach. IEEE Transactions on Medical Imaging 1999, 18:1178-1187. 10.1109/42.819327, 10695530.
8. Dodd LE, Pepe MS. Partial AUC estimation and regression. Biometrics 2003, 59:614-623. 10.1111/1541-0420.00071, 14601762.
9. Cai T, Dodd LE. Regression analysis for the partial area under the ROC curve. Statistica Sinica 2008, 18:817-836.
10. Pepe MS, Thompson ML. Combining diagnostic test results to increase accuracy. Biostatistics 2000, 1:123-140. 10.1093/biostatistics/1.2.123, 12933515.
11. Pepe MS, Cai T, Longton G. Combining predictors for classification using the area under the Receiver Operating Characteristic curve. Biometrics 2006, 62:221-229. 10.1111/j.1541-0420.2005.00420.x, 16542249.
12. Komori O. A boosting method for maximization of the area under the ROC curve. Annals of the Institute of Statistical Mathematics 2009, 2768363, 20160899.
13. Ma S, Huang J. Regularized ROC method for disease classification and biomarker selection with microarray data. Bioinformatics 2005, 21:4356-4362. 10.1093/bioinformatics/bti724, 16234316.
14. Wang Z, Chang YI, Ying Z, Zhu L, Yang Y. A parsimonious threshold-independent pretein feature selection method through the area under receiver operating characteristic curve. Bioinformatics 2007, 23:2788-1794. 10.1093/bioinformatics/btm442, 17878205.
15. Freund Y, Schapire RE. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of computer and system sciences 1997, 55:119-139.
16. Friedman J, Hastie T, Tibshirani R. Additive logistic regression: a statistical view of boosting. The Annals of Statistics 2000, 28:337-407.
17. Tutz G, Binder H. Generalized Additive modeling with implicit variable selection by likelihood-based boosting. Biometrics 2006, 62:961-971. 10.1111/j.1541-0420.2006.00578.x, 17156269.
18. Dodd LE, . (Ed) Regression methods for areas and partial areas under the ROC curve. 2001, University of Washington: Ph.D. thesis, (Ed).
19. Pepe MS, . (Ed) The Statistical Evaluation of Medical Tests for Classification and prediction. 2003, New York: Oxford University Press, (Ed).
20. Eguchi S, Copas J. A class of logistic-type discriminant functions. Biometrika 2002, 89:1-22.
21. Bühlmann P, Yu B. Boosting with the L2 loss: regression and classification. Journal of the American Statistical Association 2003, 98:324-339.
22. Murata N, Takenouchi T, Kanamori T, Eguchi S. Information geometry of U-boost and Bregman divergence. Neural Computation 2004, 16:1437-1481. 10.1162/089976604323057452, 15165397.
23. Hastie T, Tibshirani R, . (Eds) Generalized Additive Models. 1990, Chapman & Hall, (Eds).
24. McIntosh MW, Pepe MS. Combining several screening tests: Optimality of the risk score. Biometrics 2002, 58:657-664. 10.1111/j.0006-341X.2002.00657.x, 12230001.
25. Lugosi BG, Vayatis N. On the Bayes-risk consistency of regularized boosting methods. The Annals of Statistics 2004, 32:30-55.
26. Neyman J, Pearson ES. On the problem of the most efficient tests of statistical hypotheses. Philosophical Transaction of the Royal Society of London. Series A 1933, 231:289-337.
27. Pepe MS, Longton G, Anderson GL, Schummer M. Selecting differentially expressed genes from microarray experiments. Biometrics 2003, 59:133-142. 10.1111/1541-0420.00016, 12762450.
28. Zhao P, Yu B. Stagewise Lasso. Journal of Machine Learning Research 2007, 8:2701-2726.
29. Ben-Dor A, Bruhn L, Friedman N, Nachman I, Schummer M, Yakhini Z. Tissue classification with gene expression profiles. Journal of Computation Biology 2000, 7:559-583.
30. Dettling M, Bühlmann P. Boosting for tumor classification with gene expression data. Bioinformatics 2003, 19:1061-1069. 10.1093/bioinformatics/btf867, 12801866.
31. van't Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AAM, Mao M, Peterse HL, van der Kooy K, Marton MJ, Witteveen AT, Schreiber GJ, Kerkhoven RM, Roberts C, Linsley PS, Bernards R, Friend SH. Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002, 415:530-536. 10.1038/415530a, 11823860.
32. Golub TT, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, Bloomfield CD, Lander ES. Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science 1999, 286:531-537. 10.1126/science.286.5439.531, 10521349.