构建模糊决策树的自适应系统

18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397) Pub Date : 1999-06-10 DOI:10.1109/NAFIPS.1999.781687

C. Marsala, B. Bouchon-Meunier

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引用次数: 47

摘要

如今，数据挖掘是一个与数据管理和机器学习技术相关联的活跃领域。然而，即使归纳学习方法在处理符号属性时工作得很好，在考虑数字或数字符号(num/symb)属性时也会出现问题。这个问题可以通过引入模糊集理论的工具来处理这类数据来解决。本文提出了一种利用几种算子构造和使用模糊决策树的自适应系统。

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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An adaptable system to construct fuzzy decision trees

Nowadays, data mining is an active domain that is linked to data management and machine learning techniques. However, even if inductive learning methods work well when handling symbolic attributes, problems arise when considering numerical or numerical-symbolic (num/symb) attributes. This problem can be solved by introducing tools from fuzzy set theory to handle such kinds of data. In this paper, we present an adaptable system to construct and to use fuzzy decision trees by means of several kinds of operators.

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

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