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Facies identification from well logs:A comparison of discriminant analysis and naive Bayes classifier

机译:从测井中识别相:判别分析与朴素贝叶斯分类器的比较

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摘要

The performance of a naive Bayes classifier is compared with a well-established statistical classification approach,linear discriminant analysis,by considering core and log data from marine-eolian sediments.The results indicate that both methods perform adequately,and the Gaussian naive Bayes classifier provides estimates as good as those based on the linear discriminant analysis for the given data set.Quadratic discriminant analysis,a more conventional Bayesian analysis,and kernel-based density estimation methods perform unexpectedly poor,probably because of overfitting.We conclude that the normal distribution is appropriate to fit the distribution of log readings in the present data,and the simplifications of naive Bayes provide a robust,simple approach for facies identification.
机译:通过考虑海洋风沙沉积物的岩心和测井数据,将朴素贝叶斯分类器的性能与成熟的统计分类方法,线性判别分析进行了比较。结果表明这两种方法均能很好地发挥作用,高斯朴素贝叶斯分类器可以提供对于给定的数据集,线性估计的估计值与基于线性判别分析的估计值相同。二次判别分析,更常规的贝叶斯分析和基于核的密度估计方法的性能出乎意料的差,可能是因为过拟合。我们得出结论,正态分布为适当地适合当前数据中测井读数的分布,并且朴素贝叶斯的简化提供了一种可靠,简单的相识别方法。

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