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Evolutionary and Swarm Intelligence Algorithms pp 119–141Cite as

Genetic Programming for Classification and Feature Selection

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 779))

Abstract

Our objective is to provide a comprehensive introduction to Genetic Programming (GP) primarily keeping in view the problem of classifier design along with feature selection. We begin with a brief account of how genetic programming has emerged as a major computational intelligence technique. Then, we analyse classification and feature selection problems in brief. We provide a naive model of GP-based binary classification strategy with illustrative examples. We then discuss a few existing methodologies in brief and three somewhat related but different strategies with reasonable details. Before concluding, we make a few important remarks related to GP when it is used for classification and feature selection. In this context, we show some experimental results with a recent GP-based approach.

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References

  1. http://www.alanturing.net/turing_archive/archive/l/l32/L32-019.html. Accessed 17 Jan 2018

  2. Berlanga, F.J., Rivera, A., del Jesús, M.J., Herrera, F.: Gp-coach: genetic programming-based learning of compact and accurate fuzzy rule-based classification systems for high-dimensional problems. Inf. Sci. 180(8), 1183–1200 (2010)

    Article  Google Scholar 

  3. Bhowan, U., Johnston, M., Zhang, M.: Developing new fitness functions in genetic programming for classification with unbalanced data. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 42(2), 406–421 (2012)

    Article  Google Scholar 

  4. Bhowan, U., Johnston, M., Zhang, M., Yao, X.: Evolving diverse ensembles using genetic programming for classification with unbalanced data. IEEE Trans. Evol. Comput. 17(3), 368–386 (2013)

    Article  Google Scholar 

  5. Chakraborty, D., Pal, N.R.: Selecting useful groups of features in a connectionist framework. IEEE Trans. Neural Netw. 19(3), 381–396 (2008)

    Article  Google Scholar 

  6. Colorni, A., Dorigo, M., Maniezzo, V., et al.: Distributed optimization by ant colonies. In: Proceedings of the First European Conference on Artificial Life, vol. 142, pp. 134–142. Paris, France (1991)

    Google Scholar 

  7. Cramer, N.L.: A representation for the adaptive generation of simple sequential programs. In: Proceedings of the First International Conference on Genetic Algorithms, pp. 183–187 (1985)

    Google Scholar 

  8. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)

    Article  Google Scholar 

  9. Dorigo, M.: Optimization, learning and natural algorithms. Ph. D. thesis, Politecnico di Milano, Italy (1992)

    Google Scholar 

  10. Espejo, P.G., Ventura, S., Herrera, F.: A survey on the application of genetic programming to classification. IEEE Trans. Syst. Man Cybern. Part C: Appl. Rev. 40(2), 121–144 (2010)

    Article  Google Scholar 

  11. Hong, J.H., Cho, S.B.: Gene boosting for cancer classification based on gene expression profiles. Pattern Recogn. 42(9), 1761–1767 (2009)

    Article  Google Scholar 

  12. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of International Conference on Neural Networks, vol. 4., pp. 1942–1948. IEEE (Nov 1995)

    Google Scholar 

  13. Kishore, J., Patnaik, L.M., Mani, V., Agrawal, V.: Application of genetic programming for multicategory pattern classification. IEEE Trans. Evol. Comput. 4(3), 242–258 (2000)

    Article  Google Scholar 

  14. Kishore, J., Patnaik, L.M., Mani, V., Agrawal, V.: Genetic programming based pattern classification with feature space partitioning. Inf. Sci. 131(1), 65–86 (2001)

    Article  Google Scholar 

  15. Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)

    MATH  Google Scholar 

  16. Koza, J.R.: Genetic Programming II: Automatic Discovery of Reusable Programs. MIT press, Cambridge (1994)

    MATH  Google Scholar 

  17. Koza, J.R., Bennett III, F.H., Stiffelman, O.: Genetic Programming as a Darwinian Invention Machine. Springer, Berlin (1999)

    Book  Google Scholar 

  18. Koza, J.R., Keane, M.A., Streeter, M.J., Mydlowec, W., Lanza, G., Yu, J.: Genetic Programming IV: Routine Human-Competitive Machine Intelligence, vol. 5. Springer Science+Business Media (2007)

    Google Scholar 

  19. Liu, K.H., Xu, C.G.: A genetic programming-based approach to the classification of multiclass microarray datasets. Bioinformatics 25(3), 331–337 (2009)

    Article  Google Scholar 

  20. Luke, S., Panait, L.: A comparison of bloat control methods for genetic programming. Evol. Comput. 14(3), 309–344 (2006)

    Article  Google Scholar 

  21. Muni, D.P., Pal, N.R., Das, J.: A novel approach to design classifiers using genetic programming. IEEE Trans. Evol. Comput. 8(2), 183–196 (2004)

    Article  Google Scholar 

  22. Muni, D.P., Pal, N.R., Das, J.: Genetic programming for simultaneous feature selection and classifier design. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 36(1), 106–117 (2006)

    Article  Google Scholar 

  23. Nag, K., Pal, N.: A multiobjective genetic programming-based ensemble for simultaneous feature selection and classification. IEEE Trans. Cybern. 99, 1–1 (2015)

    Google Scholar 

  24. Nag, K., Pal, T., Pal, N.: ASMiGA: an archive-based steady-state micro genetic algorithm. IEEE Trans. Cybern. 45(1), 40–52 (2015)

    Article  Google Scholar 

  25. Nag, K., Pal, T.: A new archive based steady state genetic algorithm. In: 2012 IEEE Congress on Evolutionary Computation (CEC), pp. 1–7. IEEE (2012)

    Google Scholar 

  26. Poli, R.: A simple but theoretically-motivated method to control bloat in genetic programming. In: Genetic Programming, pp. 204–217. Springer, Berlin (2003)

    MATH  Google Scholar 

  27. Wang, P., Emmerich, M., Li, R., Tang, K., Baeck, T., Yao, X.: Convex hull-based multi-objective genetic programming for maximizing receiver operating characteristic performance. IEEE Trans. Evol. Comput. 99, 1–1 (2014)

    Google Scholar 

  28. Wang, P., Tang, K., Weise, T., Tsang, E., Yao, X.: Multiobjective genetic programming for maximizing roc performance. Neurocomputing 125, 102–118 (2014)

    Article  Google Scholar 

  29. Whigham, P.A., Dick, G.: Implicitly controlling bloat in genetic programming. IEEE Trans. Evol. Comput. 14(2), 173–190 (2010)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of IEE, Jadavpur University, Kolkata, India

    Kaustuv Nag

  2. ECS Unit, Indian Statistical Institute, Calcutta, India

    Nikhil R. Pal

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  1. Kaustuv Nag
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  2. Nikhil R. Pal
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Correspondence to Kaustuv Nag .

Editor information

Editors and Affiliations

  1. South Asian University, New Delhi, Delhi, India

    Jagdish Chand Bansal

  2. ABV-Indian Institute of Information Technology and Management, Gwalior, Madhya Pradesh, India

    Pramod Kumar Singh

  3. Electronics and Communication Sciences Unit (ECSU), Indian Statistical Institute, Kolkata, West Bengal, India

    Nikhil R. Pal

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Nag, K., Pal, N.R. (2019). Genetic Programming for Classification and Feature Selection. In: Bansal, J., Singh, P., Pal, N. (eds) Evolutionary and Swarm Intelligence Algorithms. Studies in Computational Intelligence, vol 779. Springer, Cham. https://doi.org/10.1007/978-3-319-91341-4_7

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