基于类关联规则的模糊系统

Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007) Pub Date : 2007-08-24 DOI:10.1109/FSKD.2007.338

R. Jia, Yibo Zhang

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引用次数: 1

摘要

模糊系统已被证明是一种普遍的逼近器，但维数诅咒仍然是模糊系统尚未解决的问题。类关联规则(CARs)是通过Apriori算法从数据中获得的有趣且频繁的模式。利用CARs建立模糊系统的模糊规则库，可以有效地解决模糊系统的维数问题。因此，本文提出了一种新的基于CARs的模糊系统。详细介绍了模糊系统的构建过程和整个系统的执行过程。通过对比实验验证了该策略的有效性。

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Fuzzy System Based on Class Association Rules

Fuzzy system has been proved to be a universal approximator, yet the curse of dimensionality is still the unsolved problem for it. Class association rules (CARs) are interesting and frequent patterns derived from data through adapted Apriori algorithm. Using CARs to build the fuzzy rule base of fuzzy system can solve the curse of dimensionality problem effectively. Thus, a novel fuzzy system based on CARs is proposed in this paper. The process of how to build the fuzzy system and its whole execution are presented in detail. Furthermore, comparative experiments are also made to prove the effectiveness of the presented strategy.

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Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007)

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