Fuzzy rule extraction using hybrid evolutionary models for data mining systems

Author

Edalat, Ilnaz ; Abadeh, Mohammad S. ; Nayyerirad, Ali

Author_Institution

Comp. Eng. Dept., IAU, Dezfoul, Iran

fYear

2011

fDate

15-17 May 2011

Firstpage

1

Lastpage

5

Abstract

Data mining is a very popular technique which is successfully used in many areas. The aim of this paper is to present a data mining system for extracting knowledge from input datasets. We use the hybrid ant colony and simulated annealing algorithms to optimize extracted fuzzy rule set. The proposed method has the main feature of data mining techniques which is high accuracy. The proposed method is then implemented on UCI datasets. The results are compared with those of well-known methods, and show the competitive systems efficiency.

Keywords

data mining; evolutionary computation; knowledge acquisition; simulated annealing; UCI datasets; data mining; fuzzy rule extraction; hybrid ant colony; hybrid evolutionary models; knowledge Extraction; simulated annealing algorithms; Accuracy; Ant colony optimization; Classification algorithms; Data mining; Equations; Simulated annealing; Training; Ant colony Algorithm; Classification; Data mining; Fuzzy rules; Simulated Annealing;

fLanguage

English

Publisher

ieee

Conference_Titel

Electro/Information Technology (EIT), 2011 IEEE International Conference on

Conference_Location

Mankato, MN

ISSN

2154-0357

Print_ISBN

978-1-61284-465-7

Type

conf

DOI

10.1109/EIT.2011.5978597

Filename

5978597