Learning using privileged information (LUPI) for modeling survival data

Han Tai Shiao, Vladimir Cherkassky

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

Abstract

Survival data is common in medical applications. The challenge in applying predictive data-analytic methods to survival data is in the treatment of censored observations, since the survival times for these observations are unknown. This paper presents formalization of the analysis of survival data as a binary classification problem. For this binary classification setting, we propose a strategy for encoding censored data, leading to the SVM/LUPI formulations. Further, we present empirical comparison of the new method and the classical Cox modeling approach for predictive modeling of survival data. These comparisons suggest that for data sets with large amount of censored data, the proposed method consistently yields better predictive performance than classical statistical modeling.

Original languageEnglish (US)
Title of host publicationProceedings of the International Joint Conference on Neural Networks
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1042-1049
Number of pages8
ISBN (Electronic)9781479914845
DOIs
StatePublished - Sep 3 2014
Event2014 International Joint Conference on Neural Networks, IJCNN 2014 - Beijing, China
Duration: Jul 6 2014Jul 11 2014

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Other

Other2014 International Joint Conference on Neural Networks, IJCNN 2014
CountryChina
CityBeijing
Period7/6/147/11/14

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