A linguistic fuzzy-XCS classifier system

被引：0

作者：

Marin-Blazquez, Javier G. ^{[1
]}

Perez, Gregorio Martinez ^{[1
]}

Perez, Manuel Gil ^{[1
]}

机构：

[1] Univ Murcia, Fac Informat, DIIC, E-30071 Murcia, Spain

来源：

2007 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-4 | 2007年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Data-driven construction of fuzzy systems has followed two different approaches. One approach is termed precise (or approximative) fuzzy modelling, that aims at numerical approximation of functions by rules, but that pays little attention to the interpretability of the resulting rule base. On the other side is linguistic (or descriptive) fuzzy modelling, that aims at automatic rule extraction but that uses fixed human provided and linguistically labelled fuzzy sets. This work follows the linguistic fuzzy modelling approach. It uses an eXtended Classifier System (XCS) as mechanism to extract linguistic fuzzy rules. XCS is one of the most successful accuracy-based learning classifier systems. It provides several mechanisms for rule generalization and also allows for online training if necessary. It can be used in sequential and non-sequential tasks. Although originally applied in discrete domains it has been extended to continuous and fuzzy environments. The proposed Linguistic Fuzzy XCS has been applied to several well-known classification problems and the results compared with both, precise and linguistic fuzzy models.

引用

页码：1531 / 1536

页数：6

共 50 条

[31] Fuzzy linguistic rules classifier for wooden board color sorting [J].

Schmitt, E ;

Bombardier, V ;

Vogrig, R .

ADVANCED CONCEPTS FOR INTELLIGENT VISION SYSTEMS, PROCEEDINGS, 2005, 3708 :154-161

[32] Kernel-based, ellipsoidal conditions in the real-valued XCS classifier system [J].

Butz, Martin V. .

GECCO 2005: GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, VOLS 1 AND 2, 2005, :1835-1842

[33] Using raw accuracy to estimate classifier fitness in XCS [J].

Lanzi, PL .

GENETIC AND EVOLUTIONARY COMPUTATION - GECCO 2003, PT II, PROCEEDINGS, 2003, 2724 :1922-1923

[34] Robust Stress Classifier Using Adaptive Neuro-Fuzzy Classifier-Linguistic Hedges [J].

Mand, Ali Afzalian ;

Wen, Justine Seow Jia ;

Sayeed, Md. Shohel ;

Swee, Sim Kok .

2017 INTERNATIONAL CONFERENCE ON ROBOTICS, AUTOMATION AND SCIENCES (ICORAS), 2017,

[35] Deep Takagi-Sugeno-Kang Fuzzy Classifier With Shared Linguistic Fuzzy Rules [J].

Zhang, Yuanpeng ;

Ishibuchi, Hisao ;

Wang, Shitong .

IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2018, 26 (03) :1535-1549

[36] Distributed classifier migration in XCS for classification of electroencephalographic signals [J].

Skinner, B. T. ;

Nguyen, H. T. ;

Liu, D. K. .

2007 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-10, PROCEEDINGS, 2007, :2829-2836

[37] Reinforcement learning in the fuzzy classifier system [J].

Valenzuela-Rendon, M .

EXPERT SYSTEMS WITH APPLICATIONS, 1998, 14 (1-2) :237-247

[38] A novel fuzzy classifier ensemble system [J].

Yang, Ai-Min ;

Jiang, Ling-Min ;

Li, Xin-Guang ;

Zhou, Yong-Mei .

PROCEEDINGS OF 2007 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7, 2007, :3582-3587

[39] Explorations in fuzzy classifier system architectures [J].

Pipe, AG ;

Carse, B .

ADAPTIVE COMPUTING IN DESIGN AND MANUFACTURE V, 2002, :219-230

[40] LINGUISTIC IMAGE OF NATURE - BURMESE NUMERATIVE CLASSIFIER SYSTEM [J].

BECKER, AL .

LINGUISTICS, 1975, (165) :109-121

← 1 2 3 4 5 →