Subscribe to RSS
Please copy the URL and add it into your RSS Feed Reader.
https://www.thieme-connect.de/rss/thieme/en/10.1055-s-00035037.xml
Download PDF
Methods Inf Med 2010; 49(03): 205-206
DOI: 10.1055/s-0038-1625340
DOI: 10.1055/s-0038-1625340
Editorial
On Novel Approaches for Classification
A Proposal for an Interdisciplinary DebateFurther Information
Publication History
Publication Date:
17 January 2018 (online)
-
References
- 1 Stollhoff R, Sauerbrei W, Schumacher M. An experimental evaluation of boosting methods for classification. Methods Inf Med 2010; 49 (03) 219-229 (this issue).
- 2 Fraiwan L, Lweesy K, Khasawneh N, Fraiwan M, Wenz H, Dickhaus H. Classification of sleep stages using multi-wavelet time frequency entropy and LDA. Methods Inf Med 2010; 49 (03) 230-237 (this issue).
- 3 Adler W, Peters A, Lausen B. Comparison of classifiers applied to confocal scanning laser ophthalmoscopy data. Methods Inf Med 2008; 47 (01) 38-46.
- 4 Tortajada S, Garcia-Gomez JM, Vicente J, Sanjuan J, de Frutos R, Martin-Santos R. et al. Prediction of postpartum depression using multilayer perceptrons and pruning. Methods Inf Med 2009; 48 (03) 291-298.
- 5 Castellani U, Cristiani M, Daducci A, Farace P, Marzola P, Murino V. et al. DCE-MRI data analysis for cancer area classification. Methods Inf Med 2009; 48 (03) 248-253.
- 6 Karvounis EC, Tsipouras MG, Papaloukas C, Tsalikakis DG, Naka KK, Fotiadis DI. A non-invasive methodology for fetal monitoring during pregnancy. Methods Inf Med 2010; 49 (03) 238-253 (this issue).
- 7 Chuang L-Y, Yang C-S, Wu K-C, Yang C-H. Correlation- based gene selection and classification using Taguchi-BPSO. Methods Inf Med 2010; 49 (03) 254-268 (this issue).
- 8 Bielza C, Robles V, Larranaga P. Estimation of distribution algorithms as logistic regression regularizers of microarray classifiers. Methods Inf Med 2009; 48 (03) 236-241.
- 9 Campos LF, Silva AC, Barros AK. Independent component analysis and neural networks applied for classification of malignant, benign and normal tissue in digital mammography. Methods Inf Med 2007; 46 (02) 212-215.
- 10 Linder R, König IR, Weimar C, Diener HC, Pöppl SJ, Ziegler A. Two models for outcome prediction – a comparison of logistic regression and neural networks. Methods Inf Med 2006; 45 (05) 536-540.
- 11 Verduijn M, Peek N, Voorbraak V, de Jonge E, de Mol BA. Modeling length of stay as an optimized two-class prediction problem. Methods Inf Med 2007; 46 (03) 352-359.
- 12 Janssens AC, Gwinn M, Bradley LA, Oostra BA, van Duijn CM, Khoury MJ. A critical appraisal of the scientific basis of commercial genomic profiles used to assess health risks and personalize health interventions. Am J Hum Genet 2008; 82 (03) 593-599.
- 13 Hand D. Classifier technology and the illusion of progress. Stat Sci 2006; 21 (01) 1-14.
- 14 Hothorn T, Leisch F, Zeileis A, Hornik K. The design and analysis of benchmark experiments. J Comput Graph Statist 2005; 14 (03) 675-699.
- 15 Cupples LA, Beyene J, Bickeböller H, Daw EW, Fallin MD, Gauderman WJ. et al. Genetic Analysis Workshop 16: Strategies for genome-wide association study analyses. BMC Proc 2009; 3 (07) S1.
- 16 Mertens B. International competition on mass spectrometry proteomic diagnosis. Stat Appl Genet Mol Biol 2008; 7 (02) article1.
- 17 König IR, Malley JD, Pajevic S, Weimar C, Diener H-C, Ziegler A. et al. Patient-centered yes/no prognosis using learning machines. Int J Data Min Bioinform 2008; 2 (04) 289-341.
- 18 Newcombe RG. Improved confidence intervals for the difference between binomial proportions based on paired data. Stat Med 1998; 17 (22) 2635-2650.
- 19 Newcombe RG. Two-sided confidence intervals for the single proportion: comparison of seven methods. Stat Med 1998; 17 (08) 857-872.
- 20 Arminger G, Enache D. Statistical models and artificial neural networks. In: Bock HH, Polasek W. editors. Data Analysis and Informations Systems. Heidelberg: Springer; 1996. pp 243-260.