符号数据在神经模糊分类中的应用

18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397) Pub Date : 1999-06-10 DOI:10.1109/NAFIPS.1999.781751

D. Nauck

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引用次数: 20

摘要

在现实世界的数据集中，我们经常需要处理不同类型的变量。例如，数据可以是分类的或度量的。数据挖掘方法通常只能处理一种数据。即使应用模糊系统(不依赖于变量的尺度)，通常也只考虑度量数据。我们提出了一种创建混合模糊规则的学习算法-使用分类和度量变量的模糊规则。

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Using symbolic data in neuro-fuzzy classification

In real world data sets, we often have to deal with different kinds of variables. The data can be for example categorical or metric. Data mining methods can often deal with only one kind of data. Even when fuzzy systems are applied-which are not dependent on the scales of variables-usually only metric data is considered. We propose a learning algorithm that creates mixed fuzzy rules-fuzzy rules that use categorical and metric variables.

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18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397)

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