模糊逻辑在机器学习中属性范围评价与选择中的应用

2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2021-07-11 DOI:10.1109/FUZZ45933.2021.9494515

Wieslaw Paja, K. Pancerz, Barbara Pekala, J. Sarzynski

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引用次数: 3

摘要

在本文中，我们展示了如何使用属性模糊化过程来确定描述案例的属性值的各个范围的重要性。重要性是根据分类能力来确定的。该方法主要基于模糊集理论和基于粗糙集的离散化方法。此外，还对计算机辅助分类任务进行了实验研究。

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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Application of the Fuzzy Logic to Evaluation and Selection of Attribute Ranges in Machine Learning

In the paper, we show how the importance of individual ranges of values of attributes describing cases can be determined using the attribute fuzzification process. The importance is determined on the basis of classification capabilities. The described approach is based mainly on fuzzy set theory and the rough set based discretization method. Moreover, an experimental study of the computer-aided classification task is presented.

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2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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