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

机译：用于发生时间预测的右审查观测的神经网络分析

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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：介绍是一种神经网络方法，用于构建具有截取和完成的观测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，以便在不改变的情况下使用缩短的数据。另一方面，现有的神经网络方法需要更改截取数据并遭受预测输出域上的可扩展性问题。此外，缩官观察的修改扭曲了数据，以便最终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一种可行的方法。

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来源
《Workshop on E-Business》|2012年||共10页
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作者
Young U. Ryu; Jae Kyeong Kim; Kwang Hyuk Im; Hankuk Hong;
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原文格式 PDF
正文语种
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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