Finding useful fuzzy concepts for pattern classification using genetic algorithm
Finding useful fuzzy concepts for pattern classification using genetic algorithm
复制标题
DOI：
10.1016/j.ins.2004.10.002
复制
发表时间：
2005-09
期刊：
影响因子：
--
通讯作者：
Yi-Chung Hu
中科院分区：
文献类型：
--
作者：
Yi-Chung Hu
本文将模糊分类器看作是一个模糊信息检索系统。为了构建这样一个系统，模糊数据挖掘方法来确定有用的模糊概念。随后，每个类和模式可以由有用的模糊概念的模糊集合表示。从模糊信息检索的角度来看，如果模式之间存在最大程度的相似性，则可以将模式归为一类。的遗传算法（GA），其目标是找到一个紧凑的集合组成的有用的模糊概念具有高的分类能力，进一步采用自动确定参数规格，不容易指定的用户。为了评估所提出的方法的分类性能，计算机模拟进行了一些著名的分类问题，表明所提出的方法的泛化能力是可比的其他模糊分类方法。
In this paper, a fuzzy classifier is treated as a fuzzy information retrieval system. To construct such a system, a fuzzy data mining method is employed to determine useful fuzzy concepts. Subsequently, each of the classes and patterns can be represented by a fuzzy set of useful fuzzy concepts. From the viewpoint of fuzzy information retrieval, a pattern can be categorized into one class if there exists a maximum degree of similarity between them. The genetic algorithm (GA), whose objective is to find a compact set consisting of useful fuzzy concepts with high classification capability, is further employed to automatically determine parameter specifications that are not easily specified by users. To evaluate classification performance of the proposed method, computer simulations are performed on some well-known classification problems, demonstrating that the generalization ability of the proposed method is comparable to other fuzzy classification methods.