[1]邢宗义,侯远龙,贾利民.基于多目标遗传算法的模糊分类系统设计[J].东南大学学报(自然科学版),2006,36(5):725-731.[doi:10.3969/j.issn.1001-0505.2006.05.009]
 Xing Zongyi,Hou Yuanlong,Jia Limin.Design of multi-objective genetic-based fuzzy classification system[J].Journal of Southeast University (Natural Science Edition),2006,36(5):725-731.[doi:10.3969/j.issn.1001-0505.2006.05.009]
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基于多目标遗传算法的模糊分类系统设计()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
36
期数:
2006年第5期
页码:
725-731
栏目:
自动化
出版日期:
2006-09-20

文章信息/Info

Title:
Design of multi-objective genetic-based fuzzy classification system
作者:
邢宗义1 侯远龙1 贾利民2
1 南京理工大学机械工程学院, 南京 210094; 2 北京交通大学交通运输学院, 北京 100044
Author(s):
Xing Zongyi1 Hou Yuanlong1 Jia Limin2
1 School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
2 School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
关键词:
模糊分类系统 多目标遗传算法 特征变量选择 模糊划分
Keywords:
fuzzy classification system multi-objective genetic algorithm feature selection fuzzy partition
分类号:
TP18
DOI:
10.3969/j.issn.1001-0505.2006.05.009
摘要:
提出了一种基于多目标遗传算法的模糊建模方法,实现了具备最大分类精度、最少特征变量和模糊规则数目的模糊分类系统的设计.首先,为缓解维数灾难问题,采用多目标遗传算法进行特征变量的选择和论域的模糊划分,构造基于栅格划分的初始模糊分类系统.然后为减少规则数目,提高模糊系统的解释性,采用遗传算法进行规则选择,得到具备较少规则数目的精简模糊分类系统.最后为提高精确性,采用约束遗传算法对精简模糊分类系统进行整体优化,在解释性不变的前提下,提高模糊分类系统的精确性.Iris和Wine分类系统的仿真,验证了该方法的有效性.
Abstract:
An approach based on multi-objective genetic algorithm is proposed to construct fuzzy classification system with maximum precision and minimum number of features and minimum number of fuzzy rules. First, in order to relieve the problem of dimension disaster, a genetic algorithm is used to accomplish feature selection and fuzzy partition with three objectives, thus an initial fuzzy system is obtained. Second by, a genetic algorithm is employed to select fuzzy rules with two objectives to achieve a compact fuzzy system. Third by, a constrained genetic algorithm is utilized to optimize the compact fuzzy system and improve its interpretability, while its precision is preserved. The proposed approach was applied to the Iris and Wine benchmark problems, and the results verify its validity.

参考文献/References:

[1] Jang J S R,Sun C T,Mizutani E. Neuro-fuzzy and soft computing[M].New Jersey:Prentice Hall,1997:85-87,335-448.
[2] Gomez-Skarmeta A F,Delgado M,Vila M A.About the use of fuzzy clustering techniques for fuzzy model identification [J]. Fuzzy Sets and Systems,1999,106(2):179-188.
[3] Cordon O,Herrera F,Hoffmann F,et al. Genetic fuzzy systems:evolutionary tuning and learning of fuzzy rule bases [M].Singapore:World Scientific,2000:89-96.
[4] Jin Yaochu. Advanced fuzzy systems design and applications [M].New York:Physical-Verl,2003:29-37.
[5] Casillas J,Cordón O,Herrera F,et al.Interpretability issues in fuzzy modeling [M].New York:Springer,2003:3-22.
[6] Abonyi J,Roubos J A,Szeifert F.Data-driven generation of compact,accurate,and linguistically sound fuzzy classifiers based on a decision-tree initialization [J].International Journal of Approximate Reasoning,2003,32(1):1-21.
[7] Ishibuchi H,Nakashima T,Murata T.Three-objective genetics-based machine learning for linguistic rule extraction [J].Information Science,2001,136(1-4):109-133.
[8] Nauck D D.Fuzzy data analysis with NEFCLASS [J].International Journal of Approximate Reasoning,2003,32(2,3):103-130.
[9] 刘士荣,俞金寿.基于最优模糊聚类的模糊推理系统及其在产品质量估计中的应用[J].信息与控制,2000,29(3):272-279.
  Liu Shirong,Yu Jinshou.A fuzzy inference system based on optimal fuzzy cluster and its application in product quality estimation [J].Information and Control,2000,29(3):272-279.(in Chinese)
[10] 邢宗义,侯远龙,张永,等.基于模糊聚类和遗传算法的具备解释性和精确性的模糊分类系统设计[J].电子学报,2006,36(1):83-88.
  Xing Zongyi,Hou YuanLong,Zhang Yong,et al.Design of interpretable and precise fuzzy classification system based on fuzzy clustering and genetic algorithm [J].Chinese Journal of Electronic,2006,36(1):83-88.(in Chinese)
[11] Wong Ching-Chang,Chen Chia-Chong.A GA-based method for constructing fuzzy systems directly from numerical data [J].IEEE Trans on Systems,Man and Cybernetics,Part B,2000,30(6):904-911.
[12] Russo M.Genetic fuzzy learning [J].IEEE Trans Evolutionary Computation,2000,4(3):259-273.
[13] Wang Jeen-Shing,Lee C S G.Self-adaptive neuro-fuzzy inference system for classification application [J].IEEE Trans Fuzzy System,2002,10(6):790-802.
[14] Wu Tzu-Ping,Chen Shyi-Ming.A new method for constructing membership functions and fuzzy rules from training examples [J]. IEEE Trans System,Man Cybernetic:Part B,1999,29(1):25-40.
[15] Shi Yushi,Eberhart R,Chen Yaobin.Implementation of evolutionary fuzzy system [J]. IEEE Trans Fuzzy Systems,1999,7(2):109-119.
[16] 童树鸿,沈毅.基于聚类分析的模糊分类系统构造方法[J].控制与决策,2001,16(增刊):737-740,744.
  Tong Shuhong,Shen Yi.Approach to construct fuzzy classification system with clustering [J]. Control and Decision,2001,16(Sup):737-740,744.(in Chinese)
[17] Setnes M,Roubos H.GA-fuzzy modeling and classification:complexity and performance [J]. IEEE Trans Fuzzy Systems,2000,8(5):509-522.
[18] Roubos J A,Setnes M.Learning fuzzy classification rules from labeled data [J]. Information Science, 2003,150(1,2):77-93.
[19] Chang Xiaoguang,Lilly J H.Evolutionary design of a fuzzy classifier from data [J]. IEEE Trans Systems,Man,and Cybernetics:Part B,2004,34(4):1894-1906.

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备注/Memo

备注/Memo:
基金项目: 国家自然科学基金资助项目(60332020).
作者简介: 邢宗义(1974—),男,博士,副教授,xingzongyi@tom.com.
更新日期/Last Update: 2006-09-20