DocumentCode :
1631447
Title :
An analysis of evolutionary algorithms with different types of fuzzy rules in subgroup discovery
Author :
Carmona, Cristóbal José ; González, Pedro ; Jesus, M. ; Herrera, Francisco
Author_Institution :
Dept. of Comput. Sci., Univ. of Jaen, Jaen, Spain
fYear :
2009
Firstpage :
1706
Lastpage :
1711
Abstract :
The interpretability of the results obtained and the quality measures used both to extract and evaluate the rules are two key aspects of subgroup discovery. In this study, we analyse the influence of the type of rule used to extract knowledge in subgroup discovery, and the quality measures more adapted to the evolutionary algorithms for subgroup discovery developed so far. The adaptation of the NMEF-SD algorithm to extract disjunctive formal norm rules is also presented.
Keywords :
data mining; evolutionary computation; fuzzy logic; evolutionary algorithms; fuzzy rules; knowledge extraction; subgroup discovery; Algorithm design and analysis; Current measurement; Data mining; Delta modulation; Evolutionary computation; Fuzzy logic; Fuzzy systems; Genetics; Proposals; Space exploration;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Systems, 2009. FUZZ-IEEE 2009. IEEE International Conference on
Conference_Location :
Jeju Island
ISSN :
1098-7584
Print_ISBN :
978-1-4244-3596-8
Electronic_ISBN :
1098-7584
Type :
conf
DOI :
10.1109/FUZZY.2009.5277412
Filename :
5277412
Link To Document :
بازگشت