Title :
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
fDate :
6/23/1905 12:00:00 AM
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;
Conference_Titel :
Fuzzy Systems, 2001. The 10th IEEE International Conference on
Conference_Location :
Melbourne, Vic.
Print_ISBN :
0-7803-7293-X
DOI :
10.1109/FUZZ.2001.1007297
