Abstract
Prognostic models are often designed on the basis of learning sets in accordance with multivariate regression methods. Recently, the interval regression and the ranked regression methods have been developed. Both these methods are useful in modeling censored data used in survival analysis. Designing the interval regression models as well as the ranked regression models can be treated similarly as the problem of linear classifier designing and linked to the concept of linear separability used in pattern recognition. The term linear separability refers to the examination of separation of two sets by a hyperplane in a given feature space.
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References
Johnson, R.A., Wichern, D.W.: Applied Multivariate Statistical Analysis. Prentice-Hall, Inc., Englewood Cliffs (1991)
Duda, O.R., Hart, P.E., Stork, D.G.: Pattern Classification. J. Wiley, New York (2001)
Bobrowski, L.: Ranked linear models and sequential patterns recognition. Pattern Analysis & Applications 12(1), 1–7 (2009)
Gomez, G., Espinal, A., Lagakos, S.: Inference for a linear regression model with an interval-censored covariate. Statistics in Medicine 22, 409–425 (2003)
Klein, J.P., Moeschberger, M.L.: Survival Analysis, Techniques for Censored and Truncated Data. Springer, NY (1997)
Bobrowski, L.: Interval Uncertainty in CPL Models for Computer Aided Prognosis. In: Hippe, Z.S., Kulikowski, J.L., Mroczek, T. (eds.) Human-Computer Systems Interaction. Backgrounds and Applications. Advances in Soft Computing, vol. 2. Springer, Heidelberg (in the press, 2011)
Bobrowski, L.: Selection of high risk patients with ranked models based on the CPL criterion functions. In: Perner, P. (ed.) ICDM 2010. LNCS, vol. 6171, pp. 432–441. Springer, Heidelberg (2010)
Bobrowski, L.: Eksploracja danych oparta na wypukłych i odcinkowo-liniowych funkcjach kryterialnych, Data mining based on convex and piecewise linear criterion functions, Technical University Białystok (2005) (in Polish)
Bobrowski, L.: Design of piecewise linear classifiers from formal neurons by some basis exchange technique. Pattern Recognition 24(9), 863–870 (1991)
Bobrowski, L., Łukaszuk, T.: Feature selection based on relaxed linear separability. Biocybernetics and Biomedical Engineering 29(2), 43–59 (2009)
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Bobrowski, L. (2011). Prognostic Models Based on Linear Separability. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2011. Lecture Notes in Computer Science(), vol 6870. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23184-1_2
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DOI: https://doi.org/10.1007/978-3-642-23184-1_2
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