Sample size planning for classification models

Analytica Chimica Acta

Volumn 760, Issue , 2013, Pages 25-33

  Beleites, Claudia ^a   Neugebauer, Ute ^a,b   Bocklitz, Thomas ^c   Krafft, Christoph ^a   Popp, Jürgen ^a,b,c  

^a LEIBNIZ INSTITUTE OF PHOTONIC TECHNOLOGY   (Germany)

^b JENA UNIVERSITY HOSPITAL   (Germany)

^c FRIEDRICH SCHILLER UNIVERSITY JENA   (Germany)

Author keywords

Classification; Design of experiments; Learning curve; Multivariate; Small sample size; Training; Validation

Indexed keywords

CLASSIFICATION MODELS; CLASSIFIER TRAINING; DATA SETS; EXTENSIVE SIMULATIONS; LEARNING CURVES; MODEL PERFORMANCE; MULTIVARIATE; RANDOM TESTING; SAMPLE SIZES; SINGLE CELLS; SMALL SAMPLE SIZE; TEST SAMPLES; TRAINING SAMPLE; TUMOUR CELLS; VALIDATION;

CELL CULTURE; CLASSIFICATION (OF INFORMATION); COMPUTER SIMULATION; DESIGN OF EXPERIMENTS; PERSONNEL TRAINING;

SAMPLING;

ACCURACY; ARTICLE; CANCER CELL CULTURE; CELL STRAIN MCF 7; CELL TYPE; CLASSIFICATION; CONTROLLED STUDY; ERYTHROCYTE; HUMAN; HUMAN CELL; LEARNING CURVE; LEUKOCYTE; MATHEMATICAL ANALYSIS; PERFORMANCE; PRIORITY JOURNAL; RAMAN SPECTROMETRY; RANDOMIZATION; SAMPLE SIZE; SAMPLE SIZE PLANNING; SIMULATION; VALIDATION PROCESS;

CELLS, CULTURED; ERYTHROCYTES; HUMANS; LEUKOCYTES; MCF-7 CELLS; MODELS, THEORETICAL; SAMPLE SIZE; SPECTRUM ANALYSIS, RAMAN;

EID: 84871519258     PISSN: 00032670     EISSN: 18734324     Source Type: Journal    
DOI: 10.1016/j.aca.2012.11.007     Document Type: Article

References (31)

1. Mukherjee S., Tamayo P., Rogers S., Rifkin R., Engle A., Campbell C., Golub T.R., Mesirov J.P. Estimating dataset size requirements for classifying DNA microarray data. J. Comput. Biol. 2003, 10(2):119-142. 10.1089/106652703321825928.
2. Figueroa R.L., Zeng-Treitler Q., Kandula S., Ngo L.H. Predicting sample size required for classification performance. BMC Med. Inform. Decis. Mak. 2012, 12(1):8. 10.1186/1472-6947-12-8.
3. Dobbin K.K., Simon R.M. Sample size planning for developing classifiers using high-dimensional DNA microarray data. Biostatistics 2007, 8(1):101-117. 10.1093/biostatistics/kxj036.
4. Dobbin K.K., Zhao Y., Simon R.M. How large a training set is needed to develop a classifier for microarray data?. Clin Cancer Res 2008, 14(1):108-114. 10.1158/1078-0432.CCR-07-0443.
5. Jain A., Chandrasekaran B. Dimensionality and sample size considerations in pattern recognition practice. Handbook of Statistics 1982, vol. II:835-855. North-Holland, Amsterdam, (Chapter 39). P.R. Krishnaiah, L. Kanal (Eds.).
6. Raudys S., Jain A. Small sample size effects in statistical pattern recognition: recommendations for practitioners. IEEE Trans. Pattern Anal. Mach. Intell. 1991, 13:252-264. http://doi.ieeecomputersociety.org/10.1109/34.75512.
7. Kalayeh H.M., Landgrebe D.A. Predicting the required number of training samples. IEEE Trans. Pattern Anal. Mach. Intell. 1983, 5(6):664-667.
8. Neugebauer U., Bocklitz T., Clement J.H., Krafft C., Popp J. Towards detection and identification of circulating tumour cells using Raman spectroscopy. Analyst 2010, 135(12):3178-3182. 10.1039/c0an00608d.
9. Neugebauer U., Clement J.H., Bocklitz T., Krafft C., Popp J. Identification and differentiation of single cells from peripheral blood by Raman spectroscopic imaging. J. Biophotonics 2010, 3(8-9):579-587. doi:10.1002/jbio.201000020.
10. Hastie T., Tibshirani R., Friedman J. The Elements of Statistical Learning Data mining, Inference and Prediction 2009, Springer-Verlag, New York. 2nd ed.
11. Dougherty E.R., Sima C., Hua J., Hanczar B., Braga-Neto U.M. Performance of error estimators for classification. Curr. Bioinform. 2010, 5:53-67.
12. Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection. Artificial Intelligence Proceedings 14th International Joint Conference 1995, 1137-1145. Morgan Kaufmann, USA. C.S. Mellish (Ed.).
13. Beleites C., Baumgartner R., Bowman C., Somorjai R., Steiner G., Salzer R., Sowa M.G. Variance reduction in estimating classification error using sparse datasets. Chem. Intell. Lab. Syst. 2005, 79:91-100.
14. Esbensen K.H., Geladi P. Principles of proper validation: use and abuse of re-sampling for validation. J. Chemometr. 2010, 24(3-4):168-187.
15. R Development Core Team R: A Language and Environment for Statistical Computing 2011, R Foundation for Statistical Computing, Vienna, Austria, ISBN: 3-900051-07-0.
16. C. Beleites, V. Sergo, hyperSpec: A Package to Handle Hyperspectral Data Sets in R, R package v. 0.98-20120725, 2012.
17. A. Genz, F. Bretz, T. Miwa, X. Mi, F. Leisch, F. Scheipl, T. Hothorn, mvtnorm: Multivariate Normal and t Distributions, R package v. 0.9-9992, 2012.
18. Genz A., Bretz F. Computation of Multivariate Normal and t Probabilities, Lecture Notes in Statistics 2009, Springer-Verlag, Heidelberg.
19. C. Beleites, cbmodels: Collection of "combined" Models: PCA-LDA, PLS-LDA, etc., R package v. 0.5-20120731, 2012.
20. R. Wehrens, B.-H. Mevik, pls: Partial Least Squares Regression (PLSR) and Principal Component Regression (PCR), R package version 2. 1-0, 2007.
21. Venables W.N., Ripley B.D. Modern Applied Statistics with S 2002, Springer, New York, ISBN: 0-387-95457-0. 4th ed.
22. Barker M., Rayens W. Partial least squares for discrimination. J. Chemometr. 2003, 17(3):166-173.
23. Brown L., Cai T., DasGupta A. Interval estimation for a binomial proportion. Stat. Sci. 2001, 16:101-133.
24. Pires A.M., Amado C. Interval estimators for a binomial proportion: comparison of twenty methods. Revstat Stat. J. 2008, 6(2):165-197.
25. S. Dorai-Raj, binom: Binomial Confidence Intervals For Several Parameterizations, R package version 1. 0-5, 2009.
26. Jaynes E. Probability Theory: The Logic of Science 2003, Cambridge University Press, Cambridge, UK, New York, NY.
27. F.E. Harrell Jr., with contributions from many other users, Hmisc: Harrell Miscellaneous, R package v. 3.9-3, 2012.
28. Fleiss J.L., Tytun A., Ury H.K. A simple approximation for calculating sample sizes for comparing independent proportions. Biometrics 1980, 36(2):343-346. 10.2307/2529990.
29. Vorburger M., Munoz B. Simple power calculations: how do we know we are doing them the right way?. Proceedings of the Survey Research Methods Section 2006, 3809-3812.
30. Fleiss J.L., Levin B., Paik M.C. Statistical Methods for Rates and Proportions 2003, Wiley-Interscience, New Jersey. 3rd ed.
31. Wickham H. ggplot2: Elegant Graphics for Data Analysis 2009, Springer, New York.