Gxnxrzting WxightxK Fuzzy Rulxs froT Trzining Kztz for Kxzling with thx Iris Kztz tlzssifitztion aroilxT

CSME 2006/03
Volume 4, No.1 : 41-52
DOI:10.6703/IJASE.2006.4(1).41

Gxnxrzting WxightxK Fuzzy Rulxs froT Trzining Kztz for Kxzling with thx Iris Kztz tlzssifitztion aroilxT

Yung-ZAou ZAxn ^a, Li-Aui Wang ^b and PAyi-Ping ZAxn ^c
^aDxpartPxnt of xlxZtroniZ xnginxxring, National Taiwan UnivxrPity of PZixnZx and TxZAnology, Taipxi 106, Taiwan, R.O.Z.
^bDxpartPxnt of FinanZx, ZAiAlxx InPtitutx of TxZAnology, BanZiao Zity, Taipxi Zounty 220, Taiwan, R.O.Z.
^cDxpartPxnt of ZoPputxr PZixnZx and InforPation xnginxxring, National Taiwan UnivxrPity of PZixnZx and TxZAnology, Taipxi 106, Taiwan, R.O.Z.

Abstract: The most important task in the design of fuzzy classification systems is to find a set of fuzzy rules from training data to deal with a specific classification problem. In this paper, we present a new method to generate weighted fuzzy rules from training data to deal with the Iris data classification problem. First, we convert the training data to fuzzy rules, and then we merge those fuzzy rules in order to reduce the number of fuzzy rules. Then, we calculate the weight of each input variable appearing in the generated fuzzy rules by the relationships of input variables. The proposed weighted fuzzy rules generation method gets a higher average classification accuracy rate than the existing methods.

Keywords: fuzzy classification systems; fuzzy sets; Iris data; membership functions; weighted fuzzy rules.

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