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
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;
Conference_Titel :
Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-5536-9
DOI :
10.1109/CEC.1999.782599
