TY  - CONF
T1  - A comparison of genetic programming variants for data classification
T2  - Springer Lecture Notes on Computer Science
Y1  - 1999
A1  - Eggermont, J.
A1  - Eiben, A. E.
A1  - van Hemert, J. I.
ED  - D. J. Hand
ED  - J. N. Kok
ED  - M. R. Berthold
KW  - classification
KW  - data mining
KW  - genetic programming
AB  - In this paper we report the results of a comparative study on different variations of genetic programming applied on binary data classification problems. The first genetic programming variant is weighting data records for calculating the classification error and modifying the weights during the run. Hereby the algorithm is defining its own fitness function in an on-line fashion giving higher weights to `hard' records. Another novel feature we study is the atomic representation, where `Booleanization' of data is not performed at the root, but at the leafs of the trees and only Boolean functions are used in the trees' body.  As a third aspect we look at generational and steady-state models in combination of both features.
JF  - Springer Lecture Notes on Computer Science
PB  - Springer-Verlag, Berlin
SN  - 3-540-66332-0
ER  -