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A memetic algorithm with support vector machine for feature selection and classification

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Abstract

The memetic algorithm (MA) is an evolutionary metaheuristic that can be viewed as a hybrid genetic algorithm combined with some kinds of local search. In this paper, we propose a memetic algorithm combined with a support vector machine (SVM) for feature selection and classification in Data mining. The proposed approach tries to find a subset of features that maximizes the classification accuracy rate of SVM. In addition, another hybrid algorithm of MA and SVM with optimized parameters is also developed. The two versions of our proposed method are evaluated on some datasets and compared with some well-known classifiers for data classification. The computational experiments show that the hybrid method MA + SVM with optimized parameters provides competitive results and finds high quality solutions.

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References

  1. Bao Y, Hu Z, Xiong T (2013) A PSO and pattern search based memetic algorithm for SVMs parameters optimization. Neurocomputing 117:98–106

    Article  Google Scholar 

  2. Bonilla Huerta EB, Duval B, Hao JK (2006) A hybrid GA/SVM approach for gene selection and classification of microarray data. In: Rothlanf et al (eds) EvoWorkshops 2006, LNCS 3907, pp 34–44

  3. Boughaci D, Benhamou B, Drias H (2004) Solving Max-SAT problems using a memetic evolutionary metaheuristic. In: Proceedings of 2004 IEEE CIS 2004, pp 480–484

  4. Boughaci D, Benhamou B, Drias H (2009) A memetic algorithm for the optimal winner determination problem. Soft Comput 13(8–9):905–917

    Article  Google Scholar 

  5. Boughaci D, Benhamou B, Drias H (2010) Local search methods for the optimal winner determination problem in combinatorial auctions. J Math Model Algorithms 9(2):165–180

    Article  MathSciNet  Google Scholar 

  6. Breiman L, Friedman J, Olshen R, Stone C (1984) Classification and regression trees. Wadsworth, Belmont

    MATH  Google Scholar 

  7. Campbell C, Ying Y (2011) Learning with support vector machines. In: Synthesis lectures on artificial intelligence and machine learning. Morgan and Claypool Publishers, CA

  8. Chang CC, Lin CJ (2001) LIBSVM: a library for support vector machines, 2001. http://www.csie.ntu.edu.tw/~cjlin/libsvm

  9. Chang CC, Lin CJ (2001) LIBSVM: a library for support vector machines, 2001. http://www.csie.ntu.edu.tw/cjlin/papers/guide/data

  10. Caponio A, Cascella GL, Neri F, Salvatore N, Sumner M (2007) A fast adaptive memetic algorithm for online and offline control design of PMSM drives. IEEE Trans Syst Man Cybern Part B 37(1):28–41

    Article  Google Scholar 

  11. Caruana R, Freitag D (1994) Greedy attribute selection. In: Proceedings of the eleventh international conference on machine learning, ICML 1994. Morgan Kauffmann, New Brunswick, pp 28–36

  12. Chen X, Ong Y, Lim M, Tan K (2011) A multi-facet survey on memetic computation. IEEE Trans Evol Comput 15(5):591–607

    Article  Google Scholar 

  13. Frank E, Witten IH (1998) Generating accurate rule sets without global optimization. In: Shavlik J (ed) Proceedings of the fifteenth international conference machine learning (ICML 98)

  14. Friedman N, Geiger D, Goldszmidt M (1997) Bayesian network classifiers. Mach Learn 29:131–163

    Article  MATH  Google Scholar 

  15. Gao XZ, Wang X, Zenger K (2015) A memetic-inspired harmony search method in optimal wind generator design. Int J Mach Learn Cyber 6(1):43–58

    Article  Google Scholar 

  16. Hamel L (2009) Knowledge discovery with support vector machines. John Wiley and Sons Inc, Canada

    Book  Google Scholar 

  17. Han J, Kamber M (2006) Data mining concepts and techniques, 2nd edn. Morgan Kaufmann, San Francisco

    MATH  Google Scholar 

  18. Hertz JA, Krogh A, Palmer RG (1991) Introduction to the theory of neural computation. Addison-Wesley Publishing Company Inc, Redwood City

    Google Scholar 

  19. John GH, Langley P (1995) Estimating continuous distributions in Bayesian classifiers. In: Proceedings of the eleventh conference on uncertainty in artificial intelligence. Morgan Kaufman, San Mateo, pp 338–345

  20. Kecman V (2001) Learning and soft computing: support vector machines. In: Neural networks, and fuzzy logic models. The MIT press, London

  21. Kohavi R, John G (1997) Wrappers for feature subset selection. Artif Intell 97(1–2):273–324

    Article  MATH  Google Scholar 

  22. Lessmann S, Stahlbock R, Crone SF (2006) Genetic algorithms for support vector machine model selection. In: Proceedings of the international joint conference on neural networks, IJCNN 2006, part of the IEEE World Congress on Computational Intelligence, WCCI 2006. IEEE, Vancouver, pp 3063–3069

  23. Li Y, Tong Y, Bai B, Zhang Y (2007) An improved particle swarm optimization for SVM training. In: Third international conference on natural computation (ICNC 2007), pp 611–615

  24. Morrison RW, De Jong KA (2002) Measurement of population diversity. In: Collet P, Fonlupt C, Hao JK, Lutton E, Schoenauer M (eds) Proceedings of AE 2001. Lecture Notes in Computer Science 2310 proceedings. Springer, pp 31–41

  25. Moscato P (1989) On evolution search optimization genetic algorithms and martial arts: towards memetic algorithms. Caltech Concurrent Computation Program, C3P Report, 826

  26. Moscato P, Norman MG (1992) A memetic approach for the traveling salesman problem implementation of a computational ecology for combinatorial optimization on message-passing systems. In: Valero et al (eds) Parallel computing and transputer applications, pp 177–186

  27. Nekkaa M, Boughaci D (2014) Stochastic local search versus genetic algorithm for feature selection. In: Proceedings of APMOD conference 2014: international conference on applied mathematical optimization and modelling 2014

  28. Nekkaa M, Boughaci D (2012) Improving support vector machine using a stochastic local search for classification in dataMining. In: Proceedings of ICONIP 2012, Part II, LNCS 7664 proceedings, pp 168–176

  29. Quinlan JR (1992) C4.5: programs for machine learning. Morgan Kaufmann, San Mateo

    Google Scholar 

  30. Rao R, Savsani V, Vakharia D (2012) Teaching learning-based optimization: an optimization method for continuous non-linear large scale problems. Inf Sci 183:1–15

    Article  MathSciNet  Google Scholar 

  31. Tan KC, Teoh EJ, Yua Q, Goh KC (2009) A hybrid evolutionary algorithm for attribute selection in data mining. Exp Syst Appl 36:8616–8630

    Article  Google Scholar 

  32. Tang M, Yao X (2007) A memetic algorithm for VLSI floorplanning. IEEE Trans Syst Man Cybern Part B 37(1):62–69

    Article  Google Scholar 

  33. Tang J, Lim MH, Ong YS (2007) Diversity-adaptive parallel memetic algorithm for solving large scale combinatorial optimization problems. Soft Comput 11(9):873–888

    Article  Google Scholar 

  34. Vapnik V (1998) Statistical learning theory. John Wiley and Sons, New York

    MATH  Google Scholar 

  35. Vapnik V (1995) The natural of statistical learning theory. Springer, New York

    Book  Google Scholar 

  36. Waikato Environment for Knowledge Analysis (WEKA), Version 3.6. The University of Waikato, Hamilton, New Zealand [online]. Software available at http://www.cs.waikato.ac.nz/ml/weka/downloading.html. Accessed 29 Mar 2014

  37. Zhou Z, Ong YS, Lim MH, Lee BS (2007) Memetic algorithm using multi-surrogates for computationally expensive optimization problems. Soft Comput 11(10):957–971

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve the quality of the paper. They would like also to thank the developers of Waikato Environment for Knowledge Analysis (WEKA) and the Library for Support Vector Machines (LIBSVM) for the provision of the open source code.

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Correspondence to Dalila Boughaci.

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Special Issue on Hybrid Nature Inspired Algorithm: Concept, Analysis and Application.

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Nekkaa, M., Boughaci, D. A memetic algorithm with support vector machine for feature selection and classification. Memetic Comp. 7, 59–73 (2015). https://doi.org/10.1007/s12293-015-0153-2

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