Adaptability, interpretability and rule weights in fuzzy rule-based systems

被引：17
|
作者：
Riid, Andri [1 ]
Ruestern, Ennu [2 ]
机构：
[1] Tallinn Univ Technol, Lab Proact Technol, EE-19086 Tallinn, Estonia
[2] Tallinn Univ Technol, Dept Comp Control, EE-19086 Tallinn, Estonia
来源：
INFORMATION SCIENCES | 2014年 / 257卷
关键词：
Fuzzy modeling; Fuzzy control; Classification; Interpretability of fuzzy systems; MULTIOBJECTIVE EVOLUTIONARY APPROACH; CLASSIFICATION RULES; INCONSISTENT RULES; INFERENCE SYSTEMS; IDENTIFICATION; DESIGN; MODELS;
D O I：
10.1016/j.ins.2012.12.048
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper discusses interpretability in two main categories of fuzzy systems - fuzzy rule-based classifiers and interpolative fuzzy systems. Our goal is to show that the aspect of high level interpretability is more relevant to fuzzy classifiers, whereas fuzzy systems employed in modeling and control benefit more from low-level interpretability. We also discuss the interpretability-accuracy tradeoff and observe why various rule weighting schemes that have been brought into play to increase adaptability of fuzzy systems rather just increase computational overhead and seriously compromise interpretability of fuzzy systems. (C) 2013 Elsevier Inc. All rights reserved.
引用
收藏
页码：301 / 312
页数：12
相关论文
共 50 条
  • [1] Effect of rule weights in fuzzy rule-based classification systems
    Ishibuchi, H
    Nakashima, T
    [J]. NINTH IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE 2000), VOLS 1 AND 2, 2000, : 59 - 64
  • [2] Effect of rule weights in fuzzy rule-based classification systems
    Ishibuchi, H
    Nakashima, T
    [J]. IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2001, 9 (04) : 506 - 515
  • [3] Interpretability of linguistic fuzzy rule-based systems: An overview of interpretability measures
    Gacto, M. J.
    Alcala, R.
    Herrera, F.
    [J]. INFORMATION SCIENCES, 2011, 181 (20) : 4340 - 4360
  • [4] A proposed method for learning rule weights in fuzzy rule-based classification systems
    Jahromi, M. Zolghadri
    Taheri, M.
    [J]. FUZZY SETS AND SYSTEMS, 2008, 159 (04) : 449 - 459
  • [5] Interpretability Assessment of Fuzzy Rule-Based Classifiers
    Mencar, Corrado
    Castiello, Ciro
    Fanelli, Anna Maria
    [J]. FUZZY LOGIC AND APPLICATIONS, 2009, 5571 : 155 - 162
  • [6] Weights-Learning for Weighted Fuzzy Rule Interpolation in Sparse Fuzzy Rule-Based Systems
    Chen, Shyi-Ming
    Chang, Yu-Chuan
    [J]. IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ 2011), 2011, : 346 - 351
  • [7] Interpretability improvement of fuzzy rule-based classifiers via rule compression
    Riid, Andri
    Preden, Juergo-Soeren
    [J]. PROCEEDINGS OF THE 2015 CONFERENCE OF THE INTERNATIONAL FUZZY SYSTEMS ASSOCIATION AND THE EUROPEAN SOCIETY FOR FUZZY LOGIC AND TECHNOLOGY, 2015, 89 : 162 - 169
  • [8] Measuring interpretability in rule-based classification systems
    Nauck, DD
    [J]. PROCEEDINGS OF THE 12TH IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1 AND 2, 2003, : 196 - 201
  • [9] On the Interpretability of Belief Rule-Based Expert Systems
    Cao, You
    Zhou, Zhijie
    Hu, Changhua
    He, Wei
    Tang, Shuaiwen
    [J]. IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2021, 29 (11) : 3489 - 3503
  • [10] A Study on Interpretability Conditions for Fuzzy Rule-Based Classifiers
    Cannone, Raffaele
    Castiello, Ciro
    Mencar, Corrado
    Fanelli, Anna M.
    [J]. 2009 9TH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS, 2009, : 438 - 443
12345