贝叶斯和模糊分类器缺失属性值的填充

NAFIPS 2008 - 2008 Annual Meeting of the North American Fuzzy Information Processing Society Pub Date : 2008-05-19 DOI:10.1109/NAFIPS.2008.4531263

A. Ralescu, S. Visa

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引用次数: 0

摘要

多维分类问题通常必须解决缺少属性值的问题。本文讨论了在两个基于频率的分类器的情况下解决这个问题的方法。将贝叶斯方法的低频值增强或缺失值填充与模糊分类器中的插值操作进行了比较。

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On filling-in missing attribute values for Bayes and fuzzy classifiers

Multidimensional classification problems often must address the issue of missing attribute values. The solution for this problem in the case of two frequency based classifiers is discussed here. The Bayes approach of boosting low frequency values, or filling-in missing values is compared to the interpolation operation used in the fuzzy classifiers.

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NAFIPS 2008 - 2008 Annual Meeting of the North American Fuzzy Information Processing Society

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