INDUCING COOPERATION IN FUZZY CLASSIFICATION RULES USING ITERATIVE RULE LEARNING AND RULE-WEIGHTING

Omid Dehzangi, Ehsan Younessian, Fariborz Hosseini Fard

2009

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.

Paper Citation

in Harvard Style

Dehzangi O., Younessian E. and Hosseini Fard F. (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

in Bibtex Style

@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},
}

in EndNote Style

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