Improving Generalization of Fuzzy IF--THEN Rules by Maximizing Fuzzy Entropy
Improving Generalization of Fuzzy IF--THEN Rules by Maximizing Fuzzy Entropy
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DOI：
10.1109/tfuzz.2008.924342
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发表时间：
2009-06
影响因子：
11.9
通讯作者：
Xizhao Wang;Chun-Ru Dong
中科院分区：
文献类型：
--
作者：
Xizhao Wang;Chun-Ru Dong
当从数据中提取的模糊IF-THEN规则性能不理想时，我们认为需要对规则进行细化。不同于现有的大多数规则细化方法，是基于进一步减少训练误差，本文提出了一种新的规则细化方案，是基于最大化的模糊熵的训练集。新方案通过求解一个二次规划问题来实现，有望提高初始模糊IF-THEN规则的泛化能力，同时克服细化的过拟合问题。一些选定的数据库上的实验结果表明，预期的提高泛化能力和预防过拟合的训练和测试精度的比较之前和之后的改进。
When fuzzy IF-THEN rules initially extracted from data have not a satisfying performance, we consider that the rules require refinement. Distinct from most existing rule-refinement approaches that are based on the further reduction of training error, this paper proposes a new rule-refinement scheme that is based on the maximization of fuzzy entropy on the training set. The new scheme, which is realized by solving a quadratic programming problem, is expected to have the advantages of improving the generalization capability of initial fuzzy IF-THEN rules and simultaneously overcoming the overfitting of refinement. Experimental results on a number of selected databases demonstrate the expected improvement of generalization capability and the prevention of overfitting by a comparison of both training and testing accuracy before and after the refinement.