A GA-based approach to rough data model

Author

Huang, Jinjie ; Li, Shiyong

Author_Institution

Dept. of Control. Sci. & Eng., Harbin Inst. of Technol., China

Volume

3

fYear

2004

fDate

15-19 June 2004

Firstpage

1880

Abstract

A genetic algorithm (GA) approach is presented to build the rough data model (RDM), which is a new methodology introduced by Kowalczyk in 1996 to deal with the inconsistence and uncertainty in database. Genetic algorithms (GAs) play two main roles in the proposed method: one is to select the best subset of the condition attributes, the other is to choose cut points from a candidate cuts set for discretization of the continuous valued attributes. The input space is then partitioned appropriately and a mapping relation between the input product subspaces and decision classes can be established. Moreover, a restricted genetic operator is designed for GAs to utilize the domain knowledge for faster convergence. Experimental results of two examples show the effectiveness of our approach.

Keywords

convergence; data mining; data models; genetic algorithms; rough set theory; GA based approach; continuous valued attributes; convergence; data mining; database; decision classes; domain knowledge; genetic algorithm; input product subspaces; mapping relation; restricted genetic operator; rough data model; Artificial intelligence; Convergence; Data engineering; Data models; Databases; Genetic algorithms; Genetic engineering; Intelligent control; Set theory; Uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on

Print_ISBN

0-7803-8273-0

Type

conf

DOI

10.1109/WCICA.2004.1341905

Filename

1341905