Exploiting a New Interpretability Index in the Multi-Objective Evolutionary Learning of Mamdani Fuzzy Rule-based Systems

被引：4

作者：

Antonelli, Michela ^{[1
]}

Ducange, Pietro ^{[1
]}

Lazzerini, Beatrice ^{[1
]}

Marcelloni, Francesco ^{[1
]}

机构：

[1] Univ Pisa, Dipartimento Ingn Informaz, Pisa, Italy

来源：

2009 9TH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS | 2009年

关键词：

Mamdani Fuzzy Rule-based Systems; Multiobjective Evolutionary Algorithms; Interpretability Index; Accuracy-Interpretability trade-off; Piecewise Linear Transformation; ADAPTATION; ALGORITHMS; SELECTION;

D O I：

10.1109/ISDA.2009.166

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we introduce a new index for evaluating the interpretability of Mamdani fuzzy rule-based systems (MFRBSs). The index takes both the rule base complexity and the data base integrity into account. We discuss the use of this index in the multi-objective evolutionary generation of MFRBSs with different trade-offs between accuracy and interpretability. The rule base and the membership function parameters of the MFRBSs are learnt concurrently by exploiting an appropriate chromosome coding and purposely-defined genetic operators. Results on a real-world regression problem are shown and discussed.

引用

收藏

页码：115 / 120

页数：6

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