Open Access

Visualization and Comparison of Single and Combined Parametric and Nonparametric Discriminant Methods for Leukemia Type Recognition Based on Gene Expression


Cite

Ambroise, C., & McLachlan, G. J. (2002). Selection bias in gene extraction on the basis of microarray gene-expression data. Proceedings of the National Academy of Sciences of the United States of America, 99(10), 6562–6566.10.1073/pnas.102102699Search in Google Scholar

Breiman, L. (1996). Bagging predictions. Machine Learning, 24(2), 123–140.10.1007/BF00058655Search in Google Scholar

Breiman, L. (1998). Arcing classifiers. The Annals of Statistics, 26(3), 801–849.Search in Google Scholar

Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. A. (1984). Classification and regression trees. Belmont: Wadsworth.Search in Google Scholar

Dettling, M. (2004). BagBoosting for tumor classification with gene expression data. Bioinformatics, 20(18), 3583–3593.10.1093/bioinformatics/bth447Search in Google Scholar

Duda, R. O., Hart P. E., & Stork, D. G. (2001). Pattern Classification. New York: Wiley & Sons.Search in Google Scholar

Dudoit, S., Fridlyand, J., & Speed, T. P. (2002). Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data. Journal of the American Statistical Association, 97(457), 77–87.10.1198/016214502753479248Search in Google Scholar

Duin, R. P. W., Juszczak P., Paclik, P., Pekalska, E., de Ridder, D., Tax, D. M. J., & Verzakov, S. (2007). PRTools 4.1. A Matlab Toolbox for Pattern Recognition. Delft University of Technology.Search in Google Scholar

Friedman, J. (2001). Greedy Function Approximation: A Gradient Boosting Machine. The Annals of Statistics, 29(5), 1189–1232.10.1214/aos/1013203451Search in Google Scholar

Freund, Y., & Schapire, R. E. (1997). A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. Journal of Computer and System Sciences, 55(1), 119–139.10.1006/jcss.1997.1504Search in Google Scholar

Freund, Y., & Schapire, R. E. (1998). Boosting the Margin: A New Explanation for the Effectiveness of Voting Methods. The Annals of Statistics, 26(5), 1651–1686.Search in Google Scholar

Freund, Y., & Schapire, R. E. (1999). A Short Introduction to Boosting. Journal of Japanese Society for Artificial Intelligence, 14(5), 771–780,Search in Google Scholar

Golub, T. R., Slonim, D. K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J. P., Coller, H., et al. (1999). Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring. Science, 286, 531–537.10.1126/science.286.5439.531Search in Google Scholar

Hand, D. J., & Yu, K. (2001a). Idiot’s Bayes – not so stupid after all? International Statistical Review, 69(3), 385–398.10.1111/j.1751-5823.2001.tb00465.xSearch in Google Scholar

Hand, D. J., Mannila, H., & Smyth, P. (2001b). Principles of data mining. Massachusetts Institute of Technology.Search in Google Scholar

Hastie, T., Tibshirani, R., & Friedman, J. (2001). The Elements of Statistical Learning. Springer, New York.10.1007/978-0-387-21606-5Search in Google Scholar

Heijden, F., Duin, R. P. W, de Ridder, D., & Tax, D. M. J. (2004). Classification, Parameter Estimation and State Estimation. An Engineering Approach Using MATLAB. England: Wiley.10.1002/0470090154Search in Google Scholar

Kotsiantis, S. B, Zaharakis, I. D., & Pintelas, P. E. (2007). Supervised machine learning: A review of classification techniques. Artificial Intelligence Review, 26(3), 159–190.10.1007/s10462-007-9052-3Search in Google Scholar

Krzyśko, M. (1974). Kwadratowe funkcje dyskryminacyjne. Matematyka Stosowana, II, 151–156.10.14708/ma.v2i2.1057Search in Google Scholar

Krzyśko, M. (1990). Analiza dyskryminacyjna. Warszawa: WNT.Search in Google Scholar

Krzyśko, M., Wołyński, W., Górecki, T., & Skorzybut, M. (2008). Systemy uczące się: rozpoznawanie wzorców, analiza skupień i redukcja wymiarowości. Warszawa: WNT.Search in Google Scholar

Lissack, T., & Fu, K. S. (1976). Error estimation in pattern recognition via L-distance between posterior density functions. IEEE Transactions on Information Theory, 22(1), 34–45.10.1109/TIT.1976.1055512Search in Google Scholar

Marchiori, E., & Sebag, M. (2005). Bayesian Learning with Local Support Vector Machines for Cancer Classification with Gene Expression Data. In F. Rotlauf et al. (Eds) Lecture Notes in Computer Science: Vol. 3449. Applications of Evolutionary Computing (pp. 74–83). Lausanne, Switzerland. Springer Verlag.Search in Google Scholar

McLachlan, G. (2004). Discriminant Analysis and Statistical Pattern Recognition. Wiley.Search in Google Scholar

Morrison, D. F. (1990). Multivariate Statistical Methods (3rd ed.) (R. Zieliński, Trans.). New York: McGraw–Hill Book Company.Search in Google Scholar

Norusis, M. J, & SPSS, Inc. (1990). SPSS PC+ Advanced Statistics. Release 4.0. Chicago: SPSS Inc.Search in Google Scholar

Pękalska, E. (2005). The Dissimilarity representations in pattern recognition. Concepts, theory and applications (Doctoral thesis). ASCI Dissertation Series no. 109. Delft University of Technology. Delft, The Netherlands.10.1142/5965Search in Google Scholar

Pomeroy, S. L., Tamayo, P., Gaasenbeek, M., & Sturla, L. M., Angelo, M., McLaughlin, M. E., Kim, J. Y., et al. (2002). Prediction of central nervous system embryonal tumour outcome based on gene expression. Nature, 415(6870), 436–442.Search in Google Scholar

Rao, C. R. (1973). Linear Statistical Inference and its Applications (2nd Ed.). New York: Wiley.10.1002/9780470316436Search in Google Scholar

Rokach, L. (2009). Taxonomy for characterizing ensemble methods in classification tasks: A review and annotated bibliography. Computational Statistics and Data Analysis, 53(12), 4046–4072.10.1016/j.csda.2009.07.017Search in Google Scholar

Rokach, L. (2010a). Pattern Classification Using Ensemble Methods. In H. Bunke, & P. S. P. Wang (Eds.), Series in Machine Perception and Artificial Intelligence (Vol. 75). World Scientific Publishing.10.1142/7238Search in Google Scholar

Rokach, L. (2010b). Ensemble-based classifiers. Artificial Intelligence Review, 33(1–2), 1–39.10.1007/s10462-009-9124-7Search in Google Scholar

Rokach, L., & Maimon, O. (2005). Top-down induction of decision trees classifiers – a survey. IEEE Transactions on Systems, Man and Cybernetics, Part C: Applications and Reviews, 35(4), 476–487.10.1109/TSMCC.2004.843247Search in Google Scholar

SAS/STAT (1990). User’s Guide. Version 6. Cary, NC, USA: SAS Institute Inc.Search in Google Scholar

SAS/STAT (2008). 9.2 User’s Guide. Cary, NC, USA: SAS Institute Inc.Search in Google Scholar

Skurichina, M. (2001). Stabilizing weak classifiers (PhD thesis). Delft University of Technology, Delft, The Netherlands.Search in Google Scholar

Xiong, M., Li, W., Zhao, J., Jin, W., & Boerwinkle, E. (2001). Feature (Gene) Selection in Gene Expression-Based Tumor Classification. Molecular Genetics and Metabolism, 73(3), 239–247.10.1006/mgme.2001.3193Search in Google Scholar

Webb, A. R. (2002). Statistical pattern recognition. England: Wiley.Search in Google Scholar

eISSN:
2199-6059
ISSN:
0860-150X
Language:
English
Publication timeframe:
4 times per year
Journal Subjects:
Philosophy, other