Learning approximate fuzzy rules from training examples

Author

Hong, Tzung-Pei ; Wang, Tzu-Ting ; Chien, Been-Chian

Author_Institution

Dept. of Electr. Eng., Nat. Univ. of Kaohsiung, Taiwan

Volume

1

fYear

2001

fDate

6/23/1905 12:00:00 AM

Firstpage

256

Lastpage

259

Abstract

Ziarko (1993) proposed the variable precision rough set model to deal with noisy data. This model identifies the relationships among data using crisp attribute values. However, data with quantitative values are commonly seen in real-world applications. In this paper, we propose a new algorithm to produce a set of maximally general fuzzy rules for an approximate coverage of training examples based on the variable rough set model from noisy quantitative training data. The proposed algorithm first transforms each quantitative value into a fuzzy set of linguistic terms using membership functions and then calculates the fuzzy β-lower and the fuzzy β-upper approximations. The maximally general fuzzy rules for approximately covering the given data are then generated based on these fuzzy approximations by an iterative induction process

Keywords

fuzzy logic; knowledge acquisition; learning by example; noise; rough set theory; approximate fuzzy rule learning; crisp attribute values; example-based learning; fuzzy β-lower approximation; fuzzy β-upper approximation; fuzzy set; iterative induction process; linguistic terms; maximally general fuzzy rules; membership functions; noisy data; noisy quantitative training data; quantitative values; variable precision rough set model; variable rough set model; Algorithm design and analysis; Data engineering; Data mining; Expert systems; Fuzzy sets; Induction generators; Iterative algorithms; Knowledge acquisition; Machine learning; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems, 2001. The 10th IEEE International Conference on

Conference_Location

Melbourne, Vic.

Print_ISBN

0-7803-7293-X

Type

conf

DOI

10.1109/FUZZ.2001.1007297

Filename

1007297