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An Island Memetic Differential Evolution Algorithm for the Feature Selection Problem

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Part of the Studies in Computational Intelligence book series (SCI,volume 512)

Abstract

The Feature Selection Problem is an interesting and important topic which is relevant for a variety of database applications. This paper applies a hybridized version of the Differential Evolution algorithm, the Island Memetic Differential Evolution algorithm, for solving the feature subset selection problem while the Nearest Neighbor Classification method is used for the classification task. The performance of the proposed algorithm is tested using various benchmark datasets from the UCI Machine Learning Repository. The algorithm is compared with variants of the differential evolution algorithm, a particle swarm optimization algorithm, an ant colony optimization algorithm and a genetic algorithm and with a number of algorithms from the literature.

Keywords

  • Feature Selection Problem
  • Differential Evolution
  • Island Models
  • Memetic Algorithms
  • Variable Neighborhood Search

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References

  1. Al-Ani, A.: Feature subset selection using ant colony optimization. International Journal of Computational Intelligence 2(1), 53–58 (2005)

    Google Scholar 

  2. Al-Ani, A.: Ant colony optimization for feature subset selection. Transactions on Engineering, Computing and Technology 4, 35–38 (2005)

    Google Scholar 

  3. Apolloni, J., Leguizam, G., Garcia-Nieto, J., Alba, E.: Island Based Distributed Differential Evolution: An Experimental Study on Hybrid Testbeds. In: International Conference on Hybrid Intelligent Systems, pp. 696–701 (2008)

    Google Scholar 

  4. Cantú-Paz, E.: Feature subset selection, class separability, and genetic Algorithms. In: Deb, K., Tari, Z. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 959–970. Springer, Heidelberg (2004)

    CrossRef  Google Scholar 

  5. Cantu-Paz, E., Newsam, S., Kamath, C.: Feature selection in scientific application. In: Proceedings of the 2004 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 788–793 (2004)

    Google Scholar 

  6. Caponio, A., Neri, F., Tirronen, V.: Super-fit control adaptation in memetic differential evolution frameworks. Soft Computing 13, 811–831 (2009)

    CrossRef  Google Scholar 

  7. Carvalho, D.R., Freitas, A.A.: A hybrid decision tree/genetic algorithm method for data mining. Information Sciences 163(1-3), 13–35 (2004)

    CrossRef  Google Scholar 

  8. Casado Yusta, S.: Different metaheuristic strategies to solve the feature selection problem. Pattern Recognition Letters 30, 525–534 (2009)

    CrossRef  Google Scholar 

  9. Casillas, J., Cordon, O., Del Jesus, M.J., Herrera, F.: Genetic feature selection in a fuzzy rule-based classification system learning process for high-dimensional problems. Information Sciences 136(1-4), 135–157 (2001)

    MATH  CrossRef  Google Scholar 

  10. Chen, S.C., Lin, S.W., Chou, S.Y.: Enhancing the classification accuracy by scatter-search-based ensemble approach. Applied Soft Computing 11(1), 1021–1028 (2011)

    CrossRef  Google Scholar 

  11. Chen, Y., Miao, D., Wang, R.: A rough set approach to feature selection based on ant colony optimization. Pattern Recognition Letters 31, 226–233 (2010)

    CrossRef  Google Scholar 

  12. Chuang, L.Y., Yang, C.H., Li, J.C.: Chaotic maps based on binary particle swarm optimization for feature selection. Applied Soft Computing (2009), doi:10.1016/j.asoc.2009.11.014

    Google Scholar 

  13. Dorigo, M., Stutzle, T.: Ant Colony Optimizationm. A Bradford Book. The MIT Press Cambridge, Massachusetts (2004)

    Google Scholar 

  14. Dorronsoro, B., Bouvry, P.: Improving Classical and Decentralized Differential Evolution with New Mutation Operator and Population Topologies. IEEE Transactions on Evolutionary Computation 15(1), 67–98 (2011)

    CrossRef  Google Scholar 

  15. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification and Scene Analysis, 2nd edn. John Wiley and Sons, New York (2001)

    Google Scholar 

  16. ElAlami, M.E.: A filter model for feature subset selection based on genetic algorithm. Knowledge-Based Systems 22, 356–362 (2009)

    CrossRef  Google Scholar 

  17. Engelbrecht, A.P.: Computational Intelligence: An Introduction. John Wiley and Sons (2007)

    Google Scholar 

  18. Feoktistov, V.: Differential Evolution - In Search of Solutions. Springer, NY (2006)

    MATH  Google Scholar 

  19. Garcia Lopez, F., Garcia Torres, M., Melian Batista, B., Moreno Perez, J.A., Moreno Vega, J.M.: Solving feature subset selection problem by a parallel scatter search. European Journal of Operational Research 169, 477–489 (2006)

    MathSciNet  MATH  CrossRef  Google Scholar 

  20. Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)

    Google Scholar 

  21. Hsu, W.H.: Genetic wrappers for feature selection in decision tree induction and variable ordering in Bayesian network structure learning. Information Sciences 163(1-3), 103–122 (2004)

    MathSciNet  CrossRef  Google Scholar 

  22. Huang, J., Cai, Y., Xu, X.: A hybrid genetic algorithm for feature selection wrapper based on mutual information. Pattern Recognition Letters 28, 1825–1844 (2007)

    CrossRef  Google Scholar 

  23. Huang, C.L.: ACO-based hybrid classification system with feature subset selection and model parameters optimization. Neurocomputing 73, 438–448 (2009)

    CrossRef  Google Scholar 

  24. Izzo, D., Rucinski, M., Ampatzis, C.: Parallel global optimisation meta-heuristics using an asynchronous island-model. In: IEEE Congress on Evolutionary Computation (CEC 2009), pp. 2301–2308 (2009)

    Google Scholar 

  25. Jain, A., Zongker, D.: Feature Selection: Evaluation, Application, and Small Sample Performance. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 153–158 (1997)

    CrossRef  Google Scholar 

  26. Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: Proceedings of 1995 IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)

    Google Scholar 

  27. Kohavi, R., John, G.: Wrappers for Feature Subset Selection. Artificial Intelligence 97, 273–324 (1997)

    MATH  CrossRef  Google Scholar 

  28. Lin, S.W., Chen, S.C.: PSOLDA: A Particle swarm optimization approach for enhancing classification accurate rate of linear discriminant analysis. Applied Soft Computing 9, 1008–1015 (2009)

    CrossRef  Google Scholar 

  29. Lin, S.W., Lee, Z.J., Chen, S.C., Tseng, T.Y.: Parameter determination of support vector machine and feature selection using simulated annealing approach. Applied Soft Computing 8, 1505–1512 (2008)

    CrossRef  Google Scholar 

  30. Lin, S.W., Ying, K.C., Chen, S.C., Lee, Z.J.: Particle swarm optimization for parameter determination and feature selection of support vector machines. Expert Systems with Applications 35, 1817–1824 (2008)

    CrossRef  Google Scholar 

  31. Marinakis, Y., Marinaki, M., Doumpos, M., Matsatsinis, N., Zopounidis, C.: Optimization of Nearest Neighbor Classifiers via Metaheuristic Algorithms for Credit Risk Assessment. Journal of Global Optimization 42, 279–293 (2008)

    MathSciNet  MATH  CrossRef  Google Scholar 

  32. Marinakis, Y., Marinaki, M., Doumpos, M., Zopounidis, C.: Ant Colony and Particle Swarm Optimization for Financial Classification Problems. Expert Systems with Applications 36(7), 10604–10611 (2009c)

    CrossRef  Google Scholar 

  33. Mininno, E., Neri, F.: A memetic Differential Evolution approach in noisy optimization. Memetic Computing 2, 111–135 (2010)

    CrossRef  Google Scholar 

  34. Moscato, P., Cotta, C.: A Gentle Introduction to Memetic Algorithms. In: Glover, F., Kochenberger, G.A. (eds.) Handbooks of Metaheuristics, pp. 105–144. Kluwer Academic Publishers, Dordrecht (2003)

    Google Scholar 

  35. Muelas, S., La Torre, A., Pena, J.M.: A Memetic Differential Evolution Algorithm for Continuous Optimization. In: Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and Applications, pp. 1080–1084 (2009)

    Google Scholar 

  36. Neri, F., Tirronen, V.: On memetic Differential Evolution frameworks: A study of advantages and limitations in hybridization. In: IEEE Congress on Evolutionary Computation (CEC 2008), pp. 2135–2142 (2008)

    Google Scholar 

  37. Parpinelli, R.S., Lopes, H.S., Freitas, A.A.: An ant colony algorithm for classification rule discovery. In: Abbas, H., Sarker, R., Newton, C. (eds.) Data Mining: A Heuristic Approach, pp. 191–208. Idea Group Publishing, London (2002)

    Google Scholar 

  38. Pedrycz, W., Park, B.J., Pizzi, N.J.: Identifying core sets of discriminatory features using particle swarm optimization. Expert Systems with Applications 36, 4610–4616 (2009)

    CrossRef  Google Scholar 

  39. Price, K.V., Storn, R.M., Lampinen, J.A.: Differential Evolution: A Practical Approach to Global Optimization. Springer, Berlin (2005)

    Google Scholar 

  40. Rokach, L.: Genetic algorithm-based feature set partitioning for classification problems. Pattern Recognition Letters 41, 1676–1700 (2008)

    MATH  CrossRef  Google Scholar 

  41. Shelokar, P.S., Jayaraman, V.K., Kulkarni, B.D.: An ant colony classifier system: Application to some process engineering problems. Computers and Chemical Engineering 28, 1577–1584 (2004)

    CrossRef  Google Scholar 

  42. Siedlecki, W., Sklansky, J.: On automatic feature selection. International Journal of Pattern Recognition and Artificial Intelligence 2(2), 197–220 (1988)

    CrossRef  Google Scholar 

  43. Siedlecki, W., Sklansky, J.: A note on genetic algorithms for large-scale feature selection. Pattern Recognition Letters 10, 335–347 (1989)

    MATH  CrossRef  Google Scholar 

  44. Srinivasa, K.G., Venugopal, K.R., Patnaik, L.M.: A self-adaptive migration model genetic algorithm for data mining applications. Information Sciences 177(20), 4295–4313 (2007)

    MATH  CrossRef  Google Scholar 

  45. Storn, R., Price, K.: Differential Evolution - A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. Journal of Global Optimization 11(4), 341–359 (1997)

    MathSciNet  MATH  CrossRef  Google Scholar 

  46. Tirronen, V., Neri, F., Karkkainen, T., Majava, K., Rossi, T.: An Enhanced Memetic Differential Evolution in Filter Design for Defect Detection in Paper Production. Evolutionary Computation 16(4), 529–555 (2008)

    CrossRef  Google Scholar 

  47. Uncu, O., Turksen, I.B.: A novel feature selection approach: Combining feature wrappers and filters. Information Sciences 177(2), 449–466 (2007)

    MathSciNet  MATH  CrossRef  Google Scholar 

  48. Unler, A., Murat, A.: A discrete particle swarm optimization method for feature selection in binary classification problems. European Journal of Operational Research 206, 528–539 (2010)

    MATH  CrossRef  Google Scholar 

  49. Wang, Y., Feng, X.Y., Huang, Y.X., Pu, D.B., Zhou, W.G., Liang, Y.C., Zhou, C.G.: A novel quantum swarm evolutionary algorithm and its applications. Neurocomputing 70(4-6), 633–640 (2007)

    CrossRef  Google Scholar 

  50. Wang, X., Yang, J., Teng, X., Xia, W., Jensen, R.: Feature selection based on rough sets and particle swarm optimization. Pattern Recognition Letters 28, 459–471 (2007)

    CrossRef  Google Scholar 

  51. Weber, M., Neri, F., Tirronen, V.: Distributed differential evolution with explorative exploitative population families. Genetic Programming Evolvable Machines 10, 343–371 (2009)

    CrossRef  Google Scholar 

  52. Weber, M., Neri, F., Tirronen, V.: A study on scale factor/crossover interaction in distributed differential evolution. Artificial Intelligence Reviews (2011), doi:10.1007/s10462-011-9267-1

    Google Scholar 

  53. Zhang, C., Hu, H.: Ant colony optimization combining with mutual information for feature selection in support vector machines. In: Zhang, S., Jarvis, R.A. (eds.) AI 2005. LNCS (LNAI), vol. 3809, pp. 918–921. Springer, Heidelberg (2005)

    CrossRef  Google Scholar 

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Marinaki, M., Marinakis, Y. (2014). An Island Memetic Differential Evolution Algorithm for the Feature Selection Problem. In: Terrazas, G., Otero, F., Masegosa, A. (eds) Nature Inspired Cooperative Strategies for Optimization (NICSO 2013). Studies in Computational Intelligence, vol 512. Springer, Cham. https://doi.org/10.1007/978-3-319-01692-4_3

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  • DOI: https://doi.org/10.1007/978-3-319-01692-4_3

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-01691-7

  • Online ISBN: 978-3-319-01692-4

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