Fuzzy partitioning with FID3.1

Author

Janikow, Cezary Z. ; Fajfer, Maciej

Author_Institution

Dept. of Math. & Comput. Sci., Missouri Univ., St. Louis, MO, USA

fYear

1999

fDate

36342

Firstpage

467

Lastpage

471

Abstract

FID3.1 builds fuzzy decision trees, with a range of choices for fuzzy operators and inferences. Various FID algorithms are being widely used for dealing with numeric and/or imprecise data, for fuzzy classification or for generating fuzzy rules. FID 3.0 adds a number of new features, the most important being a fuzzy partitioning mechanism construction of fuzzy sets for continuous variables w/o predefined fuzzy terms. FID3.1 improves the mechanism in a number of ways. The paper describes the partitioning method and presents a few comparative experiments

Keywords

decision trees; fuzzy set theory; inference mechanisms; knowledge based systems; uncertainty handling; FID 3; FID algorithms; FID3 1; comparative experiments; continuous variables; fuzzy classification; fuzzy decision trees; fuzzy operators; fuzzy partitioning mechanism construction; fuzzy rules; fuzzy sets; imprecise data; inferences; partitioning method; Computer science; Decision trees; Fuzzy sets; Inference algorithms; Mathematics; Noise measurement; Partitioning algorithms; Supervised learning; Testing; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Information Processing Society, 1999. NAFIPS. 18th International Conference of the North American

Conference_Location

New York, NY

Print_ISBN

0-7803-5211-4

Type

conf

DOI

10.1109/NAFIPS.1999.781737

Filename

781737