Semantic interpretability in hierarchical fuzzy systems: Creating semantically decouplable hierarchies

被引:33
作者
Magdalena, L. [1 ]
机构
[1] Univ Politecn Madrid, Escuela Tecn Super Ingenieros Informat, Campus Montegancedo, E-28660 Madrid, Spain
关键词
Hierarchical fuzzy systems; Interpretability; Semantics; Complexity; Explainable artificial intelligence; MODEL; CONSTRUCTION; DESIGN; INDEX;
D O I
10.1016/j.ins.2019.05.016
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Analysing the interpretability of a fuzzy system (either hierarchical or not) involves consideration of its semantic properties and evaluation of its structural complexity. The present paper concentrates on the semantic aspects of interpretability in hierarchical fuzzy systems. Complexity reduction is also considered, but from the perspective of its interaction with semantic preservation. In that sense, the paper shows that only the use of intermediate variables with meaning (interpretable variables) will transform the complexity reduction into a real improvement in interpretability. The paper formalises this idea by introducing the concept of semantically decouplable hierarchies. Under the assumption of semantic decouplability, it is shown that the interpretability of the overall hierarchical system can be directly obtained from that of its subsystems. Consequently, the paper defines and measures the interpretability of a semantically decouplable hierarchical fuzzy system as the aggregation of the interpretability of its subsystems. Finally, several options will be considered for this aggregation process. (C) 2019 Published by Elsevier Inc.
引用
收藏
页码:109 / 123
页数:15
相关论文
共 51 条
[1]   A genetic interval type-2 fuzzy logic-based approach for generating interpretable linguistic models for the brain P300 phenomena recorded via brain-computer interfaces [J].
Alhaddad, Mohammed J. ;
Mohammed, Ahmed ;
Kamel, Mahmoud ;
Hagras, Hani .
SOFT COMPUTING, 2015, 19 (04) :1019-1035
[2]   HILK: A new methodology for designing highly interpretable linguistic knowledge bases using the fuzzy logic formalism [J].
Alonso, Jose M. ;
Magdalena, Luis ;
Guillaume, Serge .
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, 2008, 23 (07) :761-794
[3]   Looking for a good fuzzy system interpretability index: An experimental approach [J].
Alonso, Jose M. ;
Magdalena, Luis ;
Gonzalez-Rodriguez, Gil .
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, 2009, 51 (01) :115-134
[4]  
Bodenhofer U, 2003, STUD FUZZ SOFT COMP, V128, P524
[5]   Hierarchical fuzzy relational models: Linguistic interpretation and universal approximation [J].
Campello, Ricardo J. G. B. ;
Caradori do Amaral, Wagner .
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2006, 14 (03) :446-453
[6]  
Cannone R., 2011, Proceedings 2011 IEEE 5th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS 2011), P1, DOI 10.1109/GEFS.2011.5949502
[7]   The Design of Customer Satisfaction Analysis Model Based on Hierarchical Fuzzy System [J].
Cao, Huiyu ;
Liu, Bin ;
He, Jianmin .
2009 INTERNATIONAL CONFERENCE ON BUSINESS INTELLIGENCE AND FINANCIAL ENGINEERING, PROCEEDINGS, 2009, :484-488
[8]  
Casillas J, 2003, Accuracy improvements in linguistic fuzzy modelling
[9]  
Casillas J., 2003, INTERPRETABILITY ISS
[10]   Linguistic modeling by hierarchical systems of linguistic rules [J].
Cordón, O ;
Herrera, F ;
Zwir, I .
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2002, 10 (01) :2-20