分类中缺失特征问题的贝叶斯方法

Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304) Pub Date : 1999-12-07 DOI:10.1109/CDC.1999.827922

R. Lynch, P. K. Willett

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引用次数: 1

摘要

本文将贝叶斯数据约简算法(BDRA)扩展到训练数据中包含缺失值特征向量的离散测试观测值分类。使用两种方法对BDRA中的缺失特征进行建模，其中使用模拟和真实数据将性能与神经网络进行比较。总的来说，BDRA优于神经网络。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A Bayesian approach to the missing features problem in classification

In this paper, the Bayesian data reduction algorithm (BDRA) is extended to classify discrete test observations given the training data contains feature vectors which are missing values. Two methods are used to model missing features in the BDRA, where performance is compared to a neural network using both simulated and real data. In general, it is shown that the BDRA is superior to the neural network.

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Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304)

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