Title :
Fuzzy partitioning with FID3.1
Author :
Janikow, Cezary Z. ; Fajfer, Maciej
Author_Institution :
Dept. of Math. & Comput. Sci., Missouri Univ., St. Louis, MO, USA
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;
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
DOI :
10.1109/NAFIPS.1999.781737
