Abstract
The rapid growth of data and the need for its proper analysis still presents a big problem for intelligent data analysis and machine learning algorithms. In order to gain a better insight into the problem being analyzed, researchers today are trying to find solutions for reducing the dimensionality of the data, by adopting algorithms that could reveal the most informative features out of the data. For this purpose, in this paper we propose a novel feature selection method based on differential evolution with a threshold mechanism. The proposed method was tested on a phishing website classification problem and evaluated with two experiments. The experimental results revealed that the proposed method performed the best in all of the test cases.
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Brezočnik, L., Fister, I., Podgorelec, V.: Swarm intelligence algorithms for feature selection: a review. Appl. Sci. 8(9) (2018). https://doi.org/10.3390/app8091521
Fister, D., Fister, I., Jagric, T., Fister Jr., I., Brest, J.: A novel self-adaptive differential evolution for feature selection using threshold mechanism. In: IEEE SSCI2018 Symposium Series on Computational Intelligence, pp. 17–24 (2018)
Khushaba, R.N., Al-Ani, A., AlSukker, A., Al-Jumaily, A.: A combined ant colony and differential evolution feature selection algorithm. In: Dorigo, M., Birattari, M., Blum, C., Clerc, M., Stützle, T., Winfield, A.F.T. (eds.) ANTS 2008. LNCS, vol. 5217, pp. 1–12. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-87527-7_1
Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence, IJCAI 1995, vol. 2, pp. 1137–1143. Morgan Kaufmann Publishers Inc., San Francisco (1995)
Liu, D.C., Nocedal, J.: On the limited memory BFGS method for large scale optimization. Math. Program. 45(1–3), 503–528 (1989)
OpenDNS: PhishTank data archives. https://www.phishtank.com/. Accessed 21 Feb 2019
Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Storn, R., Price, K.: Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341–359 (1997)
Unler, A., Murat, A.: A discrete particle swarm optimization method for feature selection in binary classification problems. Eur. J. Oper. Res. 206(3), 528–539 (2010)
Vrbančič, G., Brezočnik, L., Mlakar, U., Fister, D., Fister Jr., I.: NiaPy: python microframework for building nature-inspired algorithms. J. Open Source Softw. 3 (2018). https://doi.org/10.21105/joss.00613
Vrbančič, G.: Phishing dataset (2019). https://github.com/GregaVrbancic/Phishing-Dataset. Accessed 23 May 2019
Zorarpacı, E., Özel, S.A.: A hybrid approach of differential evolution and artificial bee colony for feature selection. Expert Syst. Appl. 62, 91–103 (2016)
Acknowledgment
The authors acknowledge the financial support from the Slovenian Research Agency (Research Core Funding No. P2-0057).
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Brezočnik, L., Fister, I., Vrbančič, G. (2019). Applying Differential Evolution with Threshold Mechanism for Feature Selection on a Phishing Websites Classification. In: Welzer, T., et al. New Trends in Databases and Information Systems. ADBIS 2019. Communications in Computer and Information Science, vol 1064. Springer, Cham. https://doi.org/10.1007/978-3-030-30278-8_2
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DOI: https://doi.org/10.1007/978-3-030-30278-8_2
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