Abstract
One of the main objectives in survival analysis is prediction the time of failure occurrence. It is done on a base of learning sets, which contain incomplete (censored) information on patients failure times. Proposed predictors should allow to cope with censored data. In the paper the influence of censoring for the performance of dipolar tree ensemble was investigated. The prediction ability of the model was verified by several measures, such as direct and indirect estimators of absolute predictive errors: \(\tilde{D}_{S,x}\), \(\hat{D}_x\) and explained variation. The analysis is conducted on the base of artificial data, generated with different values of censoring rate.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Andersen, P.K., Borgan, O., Gill, R.D.: Statistical Models based on Counting Processes. Springer, New York (1993)
Bobrowski, L., Krȩtowska, M., Krȩtowski, M.: Design of neural classifying networks by using dipolar criterions. In: Proc. of the Third Conference on Neural Networks and Their Applications, Kule, Poland, pp. 689–694 (1997)
Cox, D.R.: Regression models and life tables (with discussion). Journal of the Royal Statistical Society B 34, 187–220 (1972)
De Laurentiis, M., Ravdin, P.M.: Survival analysis of censored data: neural network analysis detection of complex interactions between variables. Breast Cancer Research and Treatment 32(1), 113–118 (1994)
Fleming, T.R., Harrington, D.P.: Counting Processes and Survival Analysis. John Wiley & Sons, Inc, Chichester (1991)
Kaplan, E.L., Meier, P.: Nonparametric estimation from incomplete observations. Journal of the American Statistical Association 5, 457–481 (1958)
Koronacki, J., Cwik, J.: Statistical learning systems. Wydawnictwa Naukowo-Techniczne, Warsaw (2005) (in Polish)
Krȩtowska, M.: Random forest of dipolar trees for survival prediction. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds.) ICAISC 2006. LNCS (LNAI), vol. 4029, pp. 909–918. Springer, Heidelberg (2006)
Krȩtowska, M.: Ensemble of dipolar neural networks in application to survival data. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2008. LNCS (LNAI), vol. 5097, pp. 78–88. Springer, Heidelberg (2008)
Krȩtowska, M.: Prognostic abilities of dipoles based ensembles comparative analysis. Zeszyty Naukowe Politechniki Bialostockiej. Informatyka 4, 73–83 (2009)
Lapuerta, P., Azen, S.P., LaBree, L.: Use of neural networks in predicting the risk of coronary artery disease. Computers and Biomedical Research 28, 38–52 (1995)
Ripley, R.M.: Neural networks for breast cancer prognosis. PhD thesis, Department of Engineering Science, University of Oxford (1998)
Schemper, M.: Predictive accuracy and explained variation. Statistics in Medicine 22, 2299–2308 (2003)
Schemper, M., Henderson, R.: Predictive accuracy and explained variation in Cox regression. Biometrics 56, 249–255 (2000)
Xiang, A., Lapuerta, P., Ryutov, A., Buckley, J., Azen, S.: Comparison of the performance of neural network methods and Cox regression for censored survival data. Computational Statistics & Data Analysis 34, 243–257 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Krȩtowska, M. (2010). The Influence of Censoring for the Performance of Survival Tree Ensemble. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artifical Intelligence and Soft Computing. ICAISC 2010. Lecture Notes in Computer Science(), vol 6114. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13232-2_64
Download citation
DOI: https://doi.org/10.1007/978-3-642-13232-2_64
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13231-5
Online ISBN: 978-3-642-13232-2
eBook Packages: Computer ScienceComputer Science (R0)