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Using genetic programming for feature creation with a genetic algorithm feature selector

Smith, Matthew G.; Bull, Larry


Matthew G. Smith

Lawrence Bull
School Director (Research & Enterprise) and Professor


Xin Yao

Edmund K. Burke

Jos� A. Lozano

Jim Smith

Juan Juli�n Merelo-Guerv�s

John A. Bullinaria

Jonathan E. Rowe

Peter Ti?o

Ata Kab�n

Hans-Paul Schwefel


The use of machine learning techniques to automatically analyse data for information is becoming increasingly widespread. In this paper we primarily examine the use of Genetic Programming and a Genetic Algorithm to preprocess data before it is classified using the C4.5 decision tree learning algorithm. 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. A Genetic Algorithm is used to determine which such features are the most predictive. Using ten wellknown datasets we show that our approach, in comparison to C4.5 alone, provides marked improvement in a number of cases. We then examine its use with other well-known machine learning techniques.


Smith, M. G., & Bull, L. (2004). Using genetic programming for feature creation with a genetic algorithm feature selector. Lecture Notes in Artificial Intelligence, 3242, 1163-1171.

Journal Article Type Conference Paper
Conference Name International Conference on Parallel Problem Solving from Nature
Publication Date Jan 1, 2004
Journal Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Print ISSN 0302-9743
Electronic ISSN 1611-3349
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
Volume 3242
Pages 1163-1171
Series Title Lecture Notes in Computer Science
Series Number 3242
ISBN 3540230920; 9783540230922
Keywords genetic programming, feature creation, genetic algorithm feature selector
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