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Feature construction and selection using genetic programming and a genetic algorithm

Smith, Matthew G.; Bull, Larry

Authors

Matthew G. Smith

Lawrence Bull Larry.Bull@uwe.ac.uk
School Director (Research & Enterprise) and Professor



Contributors

Conor Ryan
Editor

Terence Soule
Editor

Maarten Keijzer
Editor

Edward Tsang
Editor

Riccardo Poli
Editor

Ernesto Costa
Editor

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.

Citation

Smith, M. G., & Bull, L. (2003). Feature construction and selection using genetic programming and a genetic algorithm. Lecture Notes in Artificial Intelligence, 2610, 229-237. https://doi.org/10.1007/3-540-36599-0_21

Journal Article Type Conference Paper
Publication Date Jan 1, 2003
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Peer Reviewed Not Peer Reviewed
Volume 2610
Pages 229-237
Series Title Lecture Notes in Computer Science
Series Number 2610
ISBN 354000971X; 9783540009719
DOI https://doi.org/10.1007/3-540-36599-0_21
Keywords programming techniques, computation by abstract devices, algorithm analysis and problem complexity, artificial intelligence, pattern recognition, bioinformatics
Public URL https://uwe-repository.worktribe.com/output/1074750
Publisher URL http://dx.doi.org/10.1007/3-540-36599-0_21