Article

Feature construction and selection using genetic programming and a genetic algorithm

Authors:

Matthew G. Smith,

Larry BullAuthors Info & Claims

EuroGP'03: Proceedings of the 6th European conference on Genetic programming

Pages 229 - 237

Published: 14 April 2003 Publication History

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.

References

[1]

Ahluwalia, M. & Bull, L. (1999) Co-Evolving Functions in Genetic Programming: Classification using k-nearest neighbour. In W. Banzhaf, J. Daida, G. Eiben, M-H. Garzon, J. Honavar, K. Jakeila, R. Smith (eds) GECCO-99: Proceedings of the Genetic and Evolutionary Computation Conference. Morgan Kaufmann, pp. 947-952.

Google Scholar

[2]

Dixon, P. W., Corne, D. W., & Oates, M. J. (2001) A Preliminary Investigation of Modified XCS as a Generic Data Mining Tool. In P-L. Lanzi, W. Stolzmann, S. Wilson (eds) Advances in Learning Classifier Systems. Springer, pp.133-151.

Digital Library

Google Scholar

[3]

Holland, J.H. (1975) Adaptation in Natural and Artificial Systems. Univ. Michigan.

Digital Library

Google Scholar

[4]

Kelly, J.D. & Davis, L. (1991) Hybridizing the Genetic Algorithm and the K Nearest Neighbors Classification Algorithm. In R. Belew & L. Booker (eds) Proceedings of the Fourth International Conference on Genetic Algorithms. Morgan Kaufmann, pp377-383.

Google Scholar

[5]

Koza, J.R. (1992) Genetic Programming. MIT Press.

Digital Library

Google Scholar

[6]

Mangasarian, O. L. & Musicant, D. R. (2001) Lagrangian support vector machines. Journal of Machine Learning Research 1:161-177.

Digital Library

Google Scholar

[7]

Quinlan, J.R. (1993) C4.5: Programs for Machine Learning. Morgan Kaufmann.

Digital Library

Google Scholar

[8]

Raymer, M.L., Punch, W., Goodman, E.D. & Kuhn, L. (1996) Genetic Programming for Improved Data Mining - Application to the Biochemistry of Protein Interactions. In J.R. Koza, K. Deb, M. Dorigo, D.B. Fogel, M. Garzon, H. Iba & R. Riolo (eds) Proceedings of the Second Annual Conference on Genetic Programming, Morgan Kaufmann, pp375-380.

Digital Library

Google Scholar

[9]

Siedlecki, W. & Sklansky, J. (1988) On Automatic Feature Selection. International Journal of Pattern Recognition and Artificial Intelligence 2:197-220.

Crossref

Google Scholar

[10]

Vafaie, H. & De Jong, K. 1995. Genetic Algorithms as a Tool for Restructuring Feature Space Representations. In Proceedings of the International Conference on Tools with A.I. IEEE Computer Society Press.

Digital Library

Google Scholar

[11]

Witten, I.H. & Frank, E. (2000) Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann.

Digital Library

Google Scholar

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Injadat MSalo FNassif A(2016)Data mining techniques in social mediaNeurocomputing10.1016/j.neucom.2016.06.045214:C(654-670)Online publication date: 19-Nov-2016
https://dl.acm.org/doi/10.1016/j.neucom.2016.06.045
Ahmed SZhang MPeng LXue BArnold D(2014)Multiple feature construction for effective biomarker identification and classification using genetic programmingProceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation10.1145/2576768.2598292(249-256)Online publication date: 12-Jul-2014
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Castellani M(2013)Evolutionary generation of neural network classifiers-An empirical comparisonNeurocomputing10.1016/j.neucom.2012.07.01099(214-229)Online publication date: 1-Jan-2013
https://dl.acm.org/doi/10.1016/j.neucom.2012.07.010
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Feature construction and selection using genetic programming and a genetic algorithm
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning approaches

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Published In

EuroGP'03: Proceedings of the 6th European conference on Genetic programming

April 2003

486 pages

ISBN:354000971X

Editors:
Conor Ryan
University of Limerick, Computer Science and Information Systems, Limerick, Ireland
,
Terence Soule
University of Idaho, Department of Computer Science, Moscow, ID
,
Maarten Keijzer
Free University of Amsterdam, Department of Computer Science, Amsterdam, The Netherlands
,
Edward Tsang
University of Essex, Department of Computer Science, Colchester, United Kingdom
,
Riccardo Poli
University of Essex, Department of Computer Science, Colchester, United Kingdom

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 14 April 2003

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Injadat MSalo FNassif A(2016)Data mining techniques in social mediaNeurocomputing10.1016/j.neucom.2016.06.045214:C(654-670)Online publication date: 19-Nov-2016
https://dl.acm.org/doi/10.1016/j.neucom.2016.06.045
Ahmed SZhang MPeng LXue BArnold D(2014)Multiple feature construction for effective biomarker identification and classification using genetic programmingProceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation10.1145/2576768.2598292(249-256)Online publication date: 12-Jul-2014
https://dl.acm.org/doi/10.1145/2576768.2598292
Castellani M(2013)Evolutionary generation of neural network classifiers-An empirical comparisonNeurocomputing10.1016/j.neucom.2012.07.01099(214-229)Online publication date: 1-Jan-2013
https://dl.acm.org/doi/10.1016/j.neucom.2012.07.010
Zhao QZhang DZhang LLu H(2009)Evolutionary discriminant feature extraction with application to face recognitionEURASIP Journal on Advances in Signal Processing10.1155/2009/4651932009(1-12)Online publication date: 1-Jan-2009
https://dl.acm.org/doi/10.1155/2009/465193
Shafti LPérez E(2009)Evolutionary multi-feature construction for data reductionApplied Soft Computing10.1016/j.asoc.2009.04.0039:4(1296-1303)Online publication date: 1-Sep-2009
https://dl.acm.org/doi/10.1016/j.asoc.2009.04.003
Zhang MWong P(2008)Genetic programming for medical classificationGenetic Programming and Evolvable Machines10.1007/s10710-008-9059-99:3(229-255)Online publication date: 1-Sep-2008
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Zhao QZhang DLu H(2006)A direct evolutionary feature extraction algorithm for classifying high dimensional dataProceedings of the 21st national conference on Artificial intelligence - Volume 110.5555/1597538.1597629(561-566)Online publication date: 16-Jul-2006
https://dl.acm.org/doi/10.5555/1597538.1597629
Shafti LPérez EO'Reilly U(2005)Constructive induction and genetic algorithms for learning concepts with complex interactionProceedings of the 7th annual conference on Genetic and evolutionary computation10.1145/1068009.1068317(1811-1818)Online publication date: 25-Jun-2005
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Galea MShen QLevine J(2004)Evolutionary approaches to fuzzy modelling for classificationThe Knowledge Engineering Review10.1017/S026988890400018919:1(27-59)Online publication date: 1-Mar-2004
https://dl.acm.org/doi/10.1017/S0269888904000189

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Genetic Programming with a Genetic Algorithm for Feature Construction and Selection

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Genetic Programming with a Genetic Algorithm for Feature Construction and Selection

An improved genetic algorithm for optimal feature subset selection from multi-character feature set

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