Neural Network Analysis of Right-Censored Observations for Occurrence Time Prediction

机译：发生时间预测的右删失观测值的神经网络分析

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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.

机译：引入了一种神经网络方法来构建带有审查和完整观察结果的生存时间预测模型。所提出的方法修改了标准的反向传播神经网络过程，以便可以使用未经审查的审查数据。另一方面，现有的神经网络方法需要修改检查数据，并存在预测输出域可伸缩性的问题。此外，对检查结果的修改会使数据失真，因此最终的预测结果可能不准确。初步验证表明，所提出的神经网络方法是可行的方法。

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来源
《E-Life: web-enabled convergence of commerce, work, and social life》|2011年|p.100-109|共10页
会议地点 Shanghai(CN);Shanghai(CN)
作者
Young U. Ryu; Jae Kyeong Kim; Kwang Hyuk Im; Hankuk Hong;
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作者单位

University of Texas at Dallas, Richardson, Texas, USA;

Kyung Hee University, Seoul, Korea;

Pai Chai University, Daejeon, Korea;

Dong Eui University, Busan Korea;

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会议组织
原文格式 PDF
正文语种 eng
中图分类电子贸易、网上贸易;
关键词
neural networks; survival time prediction; censored observation; data mining;

机译：神经网络;生存时间预测；审查观察；数据挖掘;

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Neural Network Analysis of Right-Censored Observations for Occurrence Time Prediction

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