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Fuzzy rule base generation for classification and its minimization via modified threshold accepting

机译：用于分类的模糊规则库生成及其通过修改阈值接受的最小化

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摘要

This paper addresses the application of a modified threshold accepting algorithm (MTA) for minimizing the number of rules in a fuzzy rule-based classification system, while guaranteeing high classification power. In terms of computational time required, the MTA outperforms the GA approaches, which are applied to this multi-objective combinatorial optimization problem in the literature. The number of rules used and the classification power are taken as the objectives. The original model of Ishibuchi et al. (IEEE Trans. Fuzzy Systems 3 1995, 260-270) is further modified by employing various aggregators such as the r-operator (compensatory and), fuzzy and a convex combinations of min and max operators in place of product and min operators. The performance of the present model is demonstrated in the case of Fisher's well-known Iris data and other data appearing in literature. Less computational time needed in all cases and beter classification rate in testing phase (in leave-one-out technique) are important contribution of the present model.
机译：本文讨论了一种改进的阈值接受算法（MTA）在最小化基于模糊规则的分类系统中规则数量的同时保证高分类能力的应用。在所需的计算时间方面，MTA优于GA方法，后者已应用于文献中的多目标组合优化问题。以使用的规则数量和分类能力为目标。 Ishibuchi等人的原始模型。 （IEEE Trans.Fuzzy Systems 3 1995，260-270）通过使用各种聚合器（如r运算符（补偿和），min和max运算符的模糊和凸组合来代替积和min运算符）进行了进一步修改。在费舍尔著名的鸢尾花数据和文献中出现的其他数据的情况下，可以证明本模型的性能。在所有情况下需要更少的计算时间和测试阶段（留一法技术）中的啤酒分类率是本模型的重要贡献。

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