Feature Construction and Selection using Genetic
Programming and a Genetic Algorithm
M. Smith, and L. Bull. Genetic Programming, Proceedings of EuroGP'2003, volume 2610 of LNCS, page 229--237. Essex, Springer-Verlag, (14-16 April 2003)
Abstract
The use of machine learning techniques to
automatically analyse data for information is becoming
increasingly widespread. In this paper we examine the
use of Genetic Programming and a Genetic Algorithm to
pre-process data before it is classified using the C4.5
decision tree learning algorithm. The Genetic
Programming is used to construct new features from
those available in the data, a potentially significant
process for data mining since it gives consideration to
hidden relationships between features. The Genetic
Algorithm is used to determine which such features are
the most predictive. Using ten well-known datasets we
show that our approach, in comparison to C4.5 alone,
provides marked improvement in a number of cases.
%0 Conference Paper
%1 smith03
%A Smith, Matthew G.
%A Bull, Larry
%B Genetic Programming, Proceedings of EuroGP'2003
%C Essex
%D 2003
%E Ryan, Conor
%E Soule, Terence
%E Keijzer, Maarten
%E Tsang, Edward
%E Poli, Riccardo
%E Costa, Ernesto
%I Springer-Verlag
%K algorithms, genetic programming
%P 229--237
%T Feature Construction and Selection using Genetic
Programming and a Genetic Algorithm
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2610&spage=229
%V 2610
%X The use of machine learning techniques to
automatically analyse data for information is becoming
increasingly widespread. In this paper we examine the
use of Genetic Programming and a Genetic Algorithm to
pre-process data before it is classified using the C4.5
decision tree learning algorithm. The Genetic
Programming is used to construct new features from
those available in the data, a potentially significant
process for data mining since it gives consideration to
hidden relationships between features. The Genetic
Algorithm is used to determine which such features are
the most predictive. Using ten well-known datasets we
show that our approach, in comparison to C4.5 alone,
provides marked improvement in a number of cases.
%@ 3-540-00971-X
@inproceedings{smith03,
abstract = {The use of machine learning techniques to
automatically analyse data for information is becoming
increasingly widespread. In this paper we examine the
use of Genetic Programming and a Genetic Algorithm to
pre-process data before it is classified using the C4.5
decision tree learning algorithm. The Genetic
Programming is used to construct new features from
those available in the data, a potentially significant
process for data mining since it gives consideration to
hidden relationships between features. The Genetic
Algorithm is used to determine which such features are
the most predictive. Using ten well-known datasets we
show that our approach, in comparison to C4.5 alone,
provides marked improvement in a number of cases.},
added-at = {2008-06-19T17:46:40.000+0200},
address = {Essex},
author = {Smith, Matthew G. and Bull, Larry},
biburl = {https://www.bibsonomy.org/bibtex/2d089752650ac901b05dd0315bed8b9d8/brazovayeye},
booktitle = {Genetic Programming, Proceedings of EuroGP'2003},
editor = {Ryan, Conor and Soule, Terence and Keijzer, Maarten and Tsang, Edward and Poli, Riccardo and Costa, Ernesto},
interhash = {27763b77413177fb10fe1036e8324d07},
intrahash = {d089752650ac901b05dd0315bed8b9d8},
isbn = {3-540-00971-X},
keywords = {algorithms, genetic programming},
month = {14-16 April},
notes = {EuroGP'2003 held in conjunction with EvoWorkshops
2003},
organisation = {EvoNet},
pages = {229--237},
publisher = {Springer-Verlag},
publisher_address = {Berlin},
series = {LNCS},
timestamp = {2008-06-19T17:51:51.000+0200},
title = {Feature Construction and Selection using Genetic
Programming and a Genetic Algorithm},
url = {http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2610&spage=229},
volume = 2610,
year = 2003
}