基于广义变精度粗糙模糊集模型的知识发现

计算机科学 ›› 2009, Vol. 36 ›› Issue (9): 157-160.

基于广义变精度粗糙模糊集模型的知识发现

孙士保,吴庆涛,普杰信,秦克云

(北京航空航天大学软件开发环境国家重点实验室北京 100191);(河南科技大学电子信息工程学院洛阳 471003);(西南交通大学智能控制开发中心成都 610031)

出版日期:2018-11-16 发布日期:2018-11-16
基金资助:
本文受国家自然科学基金(60474022)，高等学校博士学科点专项科研基金(20060673007)，河南科技大学人才科学研究基金((09001172)资助。

Knowledge Discovery Approach Based on Generalized Variable Precision Rough Fuzzy Set Model

SUN Shi-bao, WU Qing-tao, PU Jie-xin, QIN Ke-yun

Online:2018-11-16 Published:2018-11-16

摘要/Abstract

摘要： 介绍了广义Ziarko's变精度粗糙集模型和广义粗糙模糊集模型，找出了它们的不足。基于支集相对错误分类率及误差参数β(0≤β＜0.5)，提出了广义变精度粗糙模糊集模型，讨论了模型中β上、下近似算子的性质;分析了该模型与广义Pawlak' s粗糙集模型、广义Ziarko's变精度粗糙集模型和广义粗糙模糊集模型的关系;最后给出了该模型中近似约简的定义和方法，并通过实例分析说明了约简算法的有效性。

关键词: 变精度粗糙集，粗糙模糊集，广义变精度粗糙模糊集，近似约简

Abstract: Generalized Ziarko' s variable precision rough set model and generalized rough fuzzy set model were introduced and the drawbacks of them were found. Based on support relative error ratio and error parameter β(0≤β＜0.5),the generalized variable precision rough fuzzy set model was proposed. The basic properties of approximation operators were investigated. hhc relation of between this model and generalized Pawlak's rough set model,generalized Ziarko's variable precision rough set model and generalized rough fuzzy set model was analysed in detail. Finally, the definitions and the approaches of approximation reduction were discussed, an example was given to illustrate the validity of the presented algorithms.

Key words: Variable precision rough set, Rough fuzzy set, Generalized variable precision rough fuzzy set, Approximation reduction

孙士保,吴庆涛,普杰信,秦克云. 基于广义变精度粗糙模糊集模型的知识发现[J]. 计算机科学, 2009, 36(9): 157-160. https://doi.org/

SUN Shi-bao, WU Qing-tao, PU Jie-xin, QIN Ke-yun. Knowledge Discovery Approach Based on Generalized Variable Precision Rough Fuzzy Set Model[J]. Computer Science, 2009, 36(9): 157-160. https://doi.org/

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