Hybrid Fuzzy Rule-Based Classification

G. Schaefer
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Abstract

Many real world applications contain a decision making process which can be regarded as a pattern classification stage. Various pattern classification techniques have been introduced in the literature ranging from heuristic methods to intelligent soft computing techniques. In this paper, we focus on the latter and in particular on fuzzy rule-based classification algorithms.We show how an effective classifier employing fuzzy if-then rules can be generated from training data, and highlight how the introduction of class weights can be used for costsensitive classification. We also show how a training algorithm can be applied to tune the classification performance and how genetic algorithms can be used to extract a compact fuzzy rule base. We also give pointers to various applications where these methods have been employed successfully.
基于混合模糊规则的分类
许多实际应用程序都包含一个决策过程,可以将其视为模式分类阶段。从启发式方法到智能软计算技术,文献中已经介绍了各种模式分类技术。本文主要研究基于模糊规则的分类算法。我们展示了如何从训练数据中生成使用模糊if-then规则的有效分类器,并强调了如何将类权重的引入用于成本敏感分类。我们还展示了如何应用训练算法来调整分类性能,以及如何使用遗传算法来提取紧凑的模糊规则库。我们还指出了成功使用这些方法的各种应用。
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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