Cited By
View all- Li NMa LYu GXue BZhang MJin Y(2023)Survey on Evolutionary Deep Learning: Principles, Algorithms, Applications, and Open IssuesACM Computing Surveys10.1145/360370456:2(1-34)Online publication date: 15-Sep-2023
Multi objective genetic programming for feature construction in classification problems
Pages 503 - 506
Abstract
This work introduces a new technique for features construction in classification problems by means of multi objective genetic programming (MOGP). The final goal is to improve the generalization ability of the final classifier. MOGP can help in finding solutions with a better generalization ability with respect to standard genetic programming as stated in [1]. The main issue is the choice of the criteria that must be optimized by MOGP. In this work the construction of new features is guided by two criteria: the first one is the entropy of the target classes as in [7] while the second is inspired by the concept of margin used in support vector machines.
References
[1]
Castelli, M., Manzoni, L., Silva, S., Vanneschi, L.: A comparison of the generalization ability of different genetic programming frameworks. In: WCCI 2010: Proceedings of IEEE World Congress on Computational Intelligence. Springer, Heidelberg (2010).
[2]
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast elitist multi-objective genetic algorithm: Nsga-ii. IEEE Transactions on Evolutionary Computation 6, 182-197 (2000).
[3]
Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artif. Intell. 97(1-2), 273-324 (1997).
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Krawiec, K.: Genetic programming-based construction of features for machine learning and knowledge discovery tasks. Genetic Programming and Evolvable Machines 3(4), 329-343 (2002).
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Lee, C., Lee, G.G.: Information gain and divergence-based feature selection for machine learning-based text categorization. Inf. Process. Manage. 42(1), 155-165 (2006).
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Neshatian, K., Zhang, M.: Genetic programming and class-wise orthogonal transformation for dimension reduction in classification problems. In: O'Neill, M., Vanneschi, L., Gustafson, S., Esparcia Alcázar, A.I., De Falco, I., Della Cioppa, A., Tarantino, E. (eds.) EuroGP 2008. LNCS, vol. 4971, pp. 242-253. Springer, Heidelberg (2008).
[7]
Neshatian, K., Zhang, M., Johnston, M.: Feature construction and dimension reduction using genetic programming. In: Orgun, M.A., Thornton, J. (eds.) AI 2007. LNCS (LNAI), vol. 4830, pp. 160-170. Springer, Heidelberg (2007).
[8]
Poli, R., Langdon, W.B., McPhee, N.F.: A field guide to genetic programming (2008), http://lulu.com, http://www.gp-field-guide.org.uk.
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Pollard, J.H.: A handbook of numerical and statistical techniques. Cambridge University Press, Cambridge (1977).
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Shannon, C.E.: A mathematical theory of communication. SIGMOBILE Mob. Comput. Commun. Rev. 5(1), 3-55 (2001).
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Smith, M.G., Bull, L.: Genetic programming with a genetic algorithm for feature construction and selection. Genetic Programming and Evolvable Machines 6(3), 265-281 (2005).
[12]
Zhang, Y., Rockett, P.: Domain-independent feature extraction for multiclassification using multi-objective genetic programming. Pattern Analysis & Applications 13, 273-288 (2010), 10.1007/s10044-009-0154-1
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- Sapienza: Sapienza University of Rome
- Microsoft Research: Microsoft Research
- IEEE Computational Intelligence Society: IEEE Computational Intelligence Society
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Springer-Verlag
Berlin, Heidelberg
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Published: 17 January 2011
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View all- Li NMa LYu GXue BZhang MJin Y(2023)Survey on Evolutionary Deep Learning: Principles, Algorithms, Applications, and Open IssuesACM Computing Surveys10.1145/360370456:2(1-34)Online publication date: 15-Sep-2023