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: D̃S,x, D̂x and explained variation. The analysis is conducted on the base of artificial data, generated with different values of censoring rate. © 2010 Springer-Verlag.
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CITATION STYLE
Krȩtowska, M. (2010). The influence of censoring for the performance of survival tree ensemble. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6114 LNAI, pp. 524–531). https://doi.org/10.1007/978-3-642-13232-2_64
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