Interpretable fuzzy modeling using multi-objective immune-inspired optimization algorithms

Chen, Jun and Mahfouf, Mahdi (2010) Interpretable fuzzy modeling using multi-objective immune-inspired optimization algorithms. In: 2010 IEEE World Congress on Computational Intelligence, July 18-23 2010, Barcelona.

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Official URL: http://dx.doi.org/10.1109/FUZZY.2010.5584902

Abstract

Abstract—In this paper, an immune inspired multi-objective fuzzy modeling (IMOFM) mechanism is proposed specifically for high-dimensional regression problems. For such problems, high predictive accuracy is often the paramount requirement. With such a requirement in mind, however, one should also put considerable efforts in making the elicited model as interpretable as possible, which leads to a difficult optimization problem. The proposed modeling approach adopts a multi-stage modeling procedure and a variable length coding scheme to account for the enlarged search space due to the simultaneous optimization of the rule-base structure and its associated parameters. IMOFM can account for both Singleton and Mamdani Fuzzy Rule-Based Systems (FRBS) due to the carefully chosen output membership functions, the inference and the defuzzification methods. The proposed algorithm has been compared with other representatives using a simple benchmark problem, and has also been applied to a high-dimensional problem which models mechanical properties of hot rolled steels. Results confirm that IMOFM can elicit accurate and yet transparent FRBSs from quantitative data.

Item Type:Conference or Workshop Item (Paper)
Additional Information:Abstract—In this paper, an immune inspired multi-objective fuzzy modeling (IMOFM) mechanism is proposed specifically for high-dimensional regression problems. For such problems, high predictive accuracy is often the paramount requirement. With such a requirement in mind, however, one should also put considerable efforts in making the elicited model as interpretable as possible, which leads to a difficult optimization problem. The proposed modeling approach adopts a multi-stage modeling procedure and a variable length coding scheme to account for the enlarged search space due to the simultaneous optimization of the rule-base structure and its associated parameters. IMOFM can account for both Singleton and Mamdani Fuzzy Rule-Based Systems (FRBS) due to the carefully chosen output membership functions, the inference and the defuzzification methods. The proposed algorithm has been compared with other representatives using a simple benchmark problem, and has also been applied to a high-dimensional problem which models mechanical properties of hot rolled steels. Results confirm that IMOFM can elicit accurate and yet transparent FRBSs from quantitative data.
Keywords:fuzzy modeling, multi-objective optimisation, Artificial Immune Systems, interpretability
Subjects:H Engineering > H131 Automated Engineering Design
H Engineering > H650 Systems Engineering
H Engineering > H130 Computer-Aided Engineering
Divisions:College of Science > School of Engineering
ID Code:2871
Deposited By: Jun Chen
Deposited On:13 Jul 2010 15:18
Last Modified:17 Jul 2014 10:58

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