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Wrapper-Based Feature Selection Using Self-adaptive Differential Evolution

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Swarm, Evolutionary, and Memetic Computing and Fuzzy and Neural Computing (SEMCCO 2019, FANCCO 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1092))

Abstract

Knowledge discovery in databases is a comprehensive procedure which enables researchers to explore knowledge and information from raw sample data usefully. Some problems may arise during this procedure, for example the Curse of Dimensionality, where the reduction of database is desired to avoid feature redundancy or irrelevancy. In this paper, we propose a wrapper-based feature selection algorithm, consisting of an artificial neural network and self-adaptive differential evolution optimization algorithm. We test performance of the feature selection algorithm on a case study of bank marketing and show that this feature selection algorithm reduces the size of the database and simultaneously improves prediction performance on the observed problem.

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References

  1. Aggarwal, C.C.: Data Classification: Algorithms and Applications. CRC Press, Boca Raton (2014)

    Book  Google Scholar 

  2. Al-Tashi, Q., Kadir, S.J.A., Rais, H.M., Mirjalili, S., Alhussian, H.: Binary optimization using hybrid grey wolf optimization for feature selection. IEEE Access 7, 39496–39508 (2019)

    Article  Google Scholar 

  3. Apolloni, J., Leguizamón, G., Alba, E.: Two hybrid wrapper-filter feature selection algorithms applied to high-dimensional microarray experiments. Appl. Soft Comput. 38, 922–932 (2016)

    Article  Google Scholar 

  4. Bates, D.W., Saria, S., Ohno-Machado, L., Shah, A., Escobar, G.: Big data in health care: using analytics to identify and manage high-risk and high-cost patients. Health Aff. 33(7), 1123–1131 (2014)

    Article  Google Scholar 

  5. Bellman, R.: Adaptive Control Processes: A Guided Tour Princeton University Press. Princeton, New Jersey (1961)

    Book  Google Scholar 

  6. Brest, J., Greiner, S., Boškovič, B., Mernik, M., Žumer, V.: Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans. Evol. Comput. 10(6), 646–657 (2006)

    Article  Google Scholar 

  7. Cardona, L., Moreno, L.A.: Cash management cost reduction using data mining to forecast cash demand and LP to optimize resources. Memetic Comput. 4(2), 127–134 (2012)

    Article  Google Scholar 

  8. Chandrashekar, G., Sahin, F.: A survey on feature selection methods. Comput. Electr. Eng. 40(1), 16–28 (2014)

    Article  Google Scholar 

  9. Chollet, F., et al.: Keras (2015). https://keras.io

  10. Da Cunha, C., Agard, B., Kusiak, A.: Selection of modules for mass customisation. Int. J. Prod. Res. 48(5), 1439–1454 (2010)

    Article  Google Scholar 

  11. Elsalamony, H.A., Elsayad, A.M.: Bank direct marketing based on neural network. Int. J. Eng. Adv. Technol. (IJEAT) 2(6) (2013)

    Google Scholar 

  12. Ertel, W.: Introduction to Artificial Intelligence. Springer, Heidelberg (2018)

    Google Scholar 

  13. Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery in databases. AI Mag. 17(3), 37–37 (1996)

    Google Scholar 

  14. Fister, D., Fister, I., Jagrič, T., Fister Jr, I., Brest, J.: A novel self-adaptive differential evolution for feature selection using threshold mechanism. In: 2018 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 17–24. IEEE (2018)

    Google Scholar 

  15. García, S., Luengo, J., Herrera, F.: Data Preprocessing in Data Mining. Springer, New York (2015). https://doi.org/10.1007/978-3-319-10247-4

    Book  Google Scholar 

  16. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

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

    Article  Google Scholar 

  18. Labani, M., Moradi, P., Ahmadizar, F., Jalili, M.: A novel multivariate filter method for feature selection in text classification problems. Eng. Appl. Artif. Intell. 70, 25–37 (2018)

    Article  Google Scholar 

  19. Lasi, H., Fettke, P., Kemper, H.-G., Feld, T., Hoffmann, M.: Industry 4.0. Bus. Inf. Syst. Eng. 6(4), 239–242 (2014)

    Article  Google Scholar 

  20. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)

    Article  Google Scholar 

  21. Liu, H., Zhou, M.C., Liu, Q.: An embedded feature selection method for imbalanced data classification. IEEE/CAA J. Automatica Sinica 6(3), 703–715 (2019)

    Article  Google Scholar 

  22. Liu, Z.-Z., Huang, J.-W., Wang, Y., Cao, D.-S.: ECoFFeS: a software using evolutionary computation for feature selection in drug discovery. IEEE Access 6, 20950–20963 (2018)

    Article  Google Scholar 

  23. Meng, L.: Embedded feature selection accounting for unknown data heterogeneity. Expert Syst. Appl. 119, 350–361 (2019)

    Article  Google Scholar 

  24. Mafarja, M., Mirjalili, S.: Whale optimization approaches for wrapper feature selection. Appl. Soft Comput. 62, 441–453 (2018)

    Article  Google Scholar 

  25. Mallik, P., Roy, C., Maheshwari, E., Pandey, M., Rautray, S.: Analyzing student performance using data mining. In: Hu, Y.-C., Tiwari, S., Mishra, K.K., Trivedi, M.C. (eds.) Ambient Communications and Computer Systems. AISC, vol. 904, pp. 307–318. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-5934-7_28

    Chapter  Google Scholar 

  26. Moro, S., Cortez, P., Rita, P.: A data-driven approach to predict the success of bank telemarketing. Decis. Support Syst. 62, 22–31 (2014)

    Article  Google Scholar 

  27. Osanaiye, O., Cai, H., Choo, K.-K.R., Dehghantanha, A., Xu, Z., Dlodlo, M.: Ensemble-based multi-filter feature selection method for DDOS detection in cloud computing. EURASIP J. Wirel. Commun. Netw. 1, 130 (2016)

    Article  Google Scholar 

  28. Parlar, T., Acaravci, S.K.: Using data mining techniques for detecting the important features of the bank direct marketing data. Int. J. Econ. Fin. Issues 7(2), 692–696 (2017)

    Google Scholar 

  29. Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)

    MathSciNet  MATH  Google Scholar 

  30. Pullanagari, R., Kereszturi, G., Yule, I.: Integrating airborne hyperspectral, topographic, and soil data for estimating pasture quality using recursive feature elimination with random forest regression. Rem. Sens. 10(7), 1117 (2018)

    Article  Google Scholar 

  31. Ramjee, S., Gamal, A.E.: Efficient wrapper feature selection using autoencoder and model based elimination. arXiv preprint arXiv:1905.11592 (2019)

  32. Scherer, M., Smolag, J., Gaweda, A.: Predicting success of bank direct marketing by neuro-fuzzy systems. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2016. LNCS (LNAI), vol. 9693, pp. 570–576. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-39384-1_50

    Chapter  Google Scholar 

  33. Serrano-Silva, Y.O., Villuendas-Rey, Y., Yáñez-Márquez, C.: Automatic feature weighting for improving financial decision support systems. Decis. Support Syst. 107, 78–87 (2018)

    Article  Google Scholar 

  34. Simon, D.: Evolutionary Optimization Algorithms. Wiley, Hoboken (2013)

    Google Scholar 

  35. Srinivasan, U., Arunasalam, B.: Leveraging big data analytics to reduce healthcare costs. IT Prof. 15(6), 21–28 (2013)

    Article  Google Scholar 

  36. Storn, R., Price, K.: Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341–359 (1997)

    Article  MathSciNet  Google Scholar 

  37. Viktorin, A., Senkerik, R., Pluhacek, M., Kadavy, T., Zamuda, A.: Distance based parameter adaptation for success-history based differential evolution. Swarm Evol. Comput. 50, 100462 (2018)

    Article  Google Scholar 

  38. Wang, S., Tang, J., Liu, H.: Embedded unsupervised feature selection. In: Twenty-Ninth AAAI Conference on Artificial Intelligence (2015)

    Google Scholar 

  39. Wang, Y., Huang, J.-J., Zhou, N., Cao, D.-S., Dong, J., Li, H.-X.: Incorporating PLS model information into particle swarm optimization for descriptor selection in QSAR/QSPR. J. Chemom. 29(12), 627–636 (2015)

    Article  Google Scholar 

  40. Xindong, W., Zhu, X., Gong-Qing, W., Ding, W.: Data mining with big data. IEEE Trans. Knowl. Data Eng. 26(1), 97–107 (2014)

    Article  Google Scholar 

  41. Xue, B., Zhang, M., Browne, W.N., Yao, X.: A survey on evolutionary computation approaches to feature selection. IEEE Trans. Evol. Comput. 20(4), 606–626 (2016)

    Article  Google Scholar 

  42. Yu, L., Liu, H.: Feature selection for high-dimensional data: a fast correlation-based filter solution. In: Proceedings of the 20th International Conference on Machine Learning (ICML-03), pp. 856–863 (2003)

    Google Scholar 

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Authors and Affiliations

  1. Faculty of Economics and Business, University of Maribor, Razlagova 14, 2000, Maribor, Slovenia

    Dušan Fister & Timotej Jagrič

  2. Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, 2000, Maribor, Slovenia

    Iztok Fister, Iztok Fister Jr. & Janez Brest

Authors
  1. Dušan Fister
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  2. Iztok Fister
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  3. Timotej Jagrič
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  4. Iztok Fister Jr.
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  5. Janez Brest
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Corresponding author

Correspondence to Dušan Fister .

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Editors and Affiliations

  1. University of Maribor, Maribor, Slovenia

    Aleš Zamuda

  2. Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, West Bengal, India

    Swagatam Das

  3. Division of Control and Instrumentation, Nanyang Technological University, Singapore, Singapore

    Ponnuthurai Nagaratnam Suganthan

  4. Department of Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, Delhi, India

    Bijaya Ketan Panigrahi

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Fister, D., Fister, I., Jagrič, T., Fister, I., Brest, J. (2020). Wrapper-Based Feature Selection Using Self-adaptive Differential Evolution. In: Zamuda, A., Das, S., Suganthan, P., Panigrahi, B. (eds) Swarm, Evolutionary, and Memetic Computing and Fuzzy and Neural Computing. SEMCCO FANCCO 2019 2019. Communications in Computer and Information Science, vol 1092. Springer, Cham. https://doi.org/10.1007/978-3-030-37838-7_13

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  • DOI: https://doi.org/10.1007/978-3-030-37838-7_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-37837-0

  • Online ISBN: 978-3-030-37838-7

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