DocumentCode
3036952
Title
A survey of genetic feature selection in mining issues
Author
Martin-Bautista, Maria J. ; Vila, María-Amparo
Author_Institution
Dept. of Comput. Sci. & Artificial Intelligence, Granada Univ., Spain
Volume
2
fYear
1999
fDate
1999
Abstract
In this paper, we review the feature selection problem in mining issues. The application of soft computing techniques to data mining and knowledge discovery is now emerging in order to enhance the effectiveness of the traditional classification methods coming from machine learning. A survey of the approaches presented in the literature to select relevant features by using genetic algorithms is given. The different values of the genetic parameters utilized as well as the fitness functions are compared. A more detailed review of the proposals in the mining fields of databases, text and the Web is also given
Keywords
data mining; data warehouses; genetic algorithms; learning (artificial intelligence); pattern classification; Web; classification methods; data mining issues; databases; fitness functions; genetic feature selection; knowledge discovery; machine learning; soft computing techniques; text;
fLanguage
English
Publisher
ieee
Conference_Titel
Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on
Conference_Location
Washington, DC
Print_ISBN
0-7803-5536-9
Type
conf
DOI
10.1109/CEC.1999.782599
Filename
782599
Link To Document