Neural network analysis of right-censored observations for occurrence time prediction

Young U. Ryu, Jae Kyeong Kim, Kwang Hyuk Im, Hankuk Hong

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Introduced is a neural network method to build survival time prediction models with censored and completed observations. The proposed method modifies the standard back-propagation neural network process so that the censored data can be used without alteration. On the other hand, existing neural network methods require alteration of censored data and suffer from the problem of scalability on the prediction output domain. Further, the modification of the censored observations distorts the data so that the final prediction outcomes may not be accurate. Preliminary validations show that the proposed neural network method is a viable method.

Original languageEnglish
Title of host publicationE-Life
Subtitle of host publicationWeb-Enabled Convergence of Commerce, Work, and Social Life - 10th Workshop on E-Business, WEB 2011, Revised Selected Papers
PublisherSpringer Verlag
Pages100-109
Number of pages10
ISBN (Print)9783642298721
DOIs
Publication statusPublished - 2012
Event10th Workshop on E-Business on E-Life: Web-Enabled Convergence of Commerce, Work, and Social Life, WEB 2011 - Shanghai, China
Duration: 4 Dec 20114 Dec 2011

Publication series

NameLecture Notes in Business Information Processing
Volume108 LNBIP
ISSN (Print)1865-1348

Conference

Conference10th Workshop on E-Business on E-Life: Web-Enabled Convergence of Commerce, Work, and Social Life, WEB 2011
Country/TerritoryChina
CityShanghai
Period4/12/114/12/11

Keywords

  • Censored Observation
  • Data Mining
  • Neural Networks
  • Survival Time Prediction

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