Parallel genetic programming for decision tree induction

Author

Folino, Gianluigi ; Pizzuti, Clara ; Spezzano, Giandomenico

Author_Institution

DEIS, Calabria Univ., Rende, Italy

fYear

2001

fDate

7-9 Nov 2001

Firstpage

129

Lastpage

135

Abstract

A parallel genetic programming approach to induce decision trees in large data sets is presented. A population of trees is evolved by employing the genetic operators and every individual is evaluated by using a fitness function based on the J-measure. The method is able to deal with large data sets since it uses a parallel implementation of genetic programming through the grid model and an out of core technique for those data sets that do not fit in main memory. Preliminary experiments on data sets from the UCI machine learning repository give good classification outcomes and assess the scalability of the method

Keywords

decision trees; genetic algorithms; learning (artificial intelligence); parallel programming; J-measure; UCI machine learning repository; data sets; decision tree induction; fitness function; grid model; parallel genetic programming; scalability; Classification algorithms; Classification tree analysis; Data mining; Decision trees; Degradation; Genetic programming; Machine learning; Scalability; Spatial databases; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Tools with Artificial Intelligence, Proceedings of the 13th International Conference on

Conference_Location

Dallas, TX

Print_ISBN

0-7695-1417-0

Type

conf

DOI

10.1109/ICTAI.2001.974457

Filename

974457