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Authors: Omid Dehzangi 1 ; Ehsan Younessian 1 and Fariborz Hosseini Fard 2

Affiliations: 1 Nanyang Technological Universit, Singapore ; 2 SoundBuzz PTE LTD, Singapore

ISBN: 978-989-8111-99-9

Keyword(s): Fuzzy Systems, Classification, Iterative Rule Learning (IRL), Rule Weighting, ROC.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Decision Support Systems ; Fuzzy Control ; Fuzzy Systems ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Mobile Robots and Autonomous Systems ; Robotics and Automation ; Soft Computing

Abstract: Fuzzy Rule-Based Classification Systems (FRBCSs) focus on generating a compact rule-base from numerical input data for classification purposes. Iterative Rule Learning (IRL) has been proposed to reduce the search space for learning a rule-set for a specific classification problem. In this approach, a rule-set is constructed by searching for an appropriate fuzzy rule and adding it to the rule-set in each iteration. A major element of this approach is the requirement of an evaluation metric to find the best rule in each iteration. The difficulty in choosing the best rule is that the evaluation metric should be able to measure the degree of cooperation of the candidate rule with the rules found so far. This poses a major difficulty when dealing with fuzzy rules; because unlike crisp rules, each pattern is compatible with a fuzzy rule only to a certain degree. In this paper, the cooperation degree of a candidate rule is divided into the following two components: I)- The cooperation degree of the rule with other rules of the same class, II)- The cooperation degree of the rule with rules of the other classes. An IRL scheme to generate fuzzy classification rules is proposed that induces cooperation among the rules of the same class. Cooperation between the rules of different classes is handled using our proposed rule-weighting mechanism. Through a set of experiments on some benchmark data sets from UCI-ML repository, the effectiveness of the proposed scheme is shown. (More)

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Paper citation in several formats:
Dehzangi O.; Younessian E.; Hosseini Fard F. and (2009). INDUCING COOPERATION IN FUZZY CLASSIFICATION RULES USING ITERATIVE RULE LEARNING AND RULE-WEIGHTING.In Proceedings of the 6th International Conference on Informatics in Control, Automation and Robotics - Volume 3: ICINCO, ISBN 978-989-8111-99-9, pages 62-67. DOI: 10.5220/0002209900620067

@conference{icinco09,
author={Omid Dehzangi and Ehsan Younessian and Fariborz {Hosseini Fard}},
title={INDUCING COOPERATION IN FUZZY CLASSIFICATION RULES USING ITERATIVE RULE LEARNING AND RULE-WEIGHTING},
booktitle={Proceedings of the 6th International Conference on Informatics in Control, Automation and Robotics - Volume 3: ICINCO,},
year={2009},
pages={62-67},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002209900620067},
isbn={978-989-8111-99-9},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Informatics in Control, Automation and Robotics - Volume 3: ICINCO,
TI - INDUCING COOPERATION IN FUZZY CLASSIFICATION RULES USING ITERATIVE RULE LEARNING AND RULE-WEIGHTING
SN - 978-989-8111-99-9
AU - Dehzangi, O.
AU - Younessian, E.
AU - Hosseini Fard, F.
PY - 2009
SP - 62
EP - 67
DO - 10.5220/0002209900620067

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