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Binary PSO Variants for Feature Selection in Handwritten Signature Authentication
Volume 33, Issue 3 (2022), pp. 523–543
Pub. online: 5 January 2022      Type: Research Article      Open accessOpen Access
Received
1 February 2021
Accepted
1 December 2021
Published
5 January 2022

Abstract

In this paper we propose modifications of the well-known algorithm of particle swarm optimization (PSO). These changes affect the mapping of the motion of particles from continuous space to binary space for searching in it, which is widely used to solve the problem of feature selection. The modified binary PSO variations were tested on the dataset SVC2004 dedicated to the problem of user authentication based on dynamic features of a handwritten signature. In the example of k-nearest neighbours (kNN), experiments were carried out to find the optimal subset of features. The search for the subset was considered as a multicriteria optimization problem, taking into account the accuracy of the model and the number of features.

References

 
Al-Ani, A., Khushaba, R.N. (2012). A population based feature subset selection algorithm guided by fuzzy feature dependency. In: Hassanien, A.E., Salem, A.-B.M., Ramadan, R., Kim, T.-h. (Eds.), Advanced Machine Learning Technologies and Applications. Springer, Berlin, Heidelberg, pp. 430–438.
 
Arora, S., Anand, P. (2019). Binary butterfly optimization approaches for feature selection. Expert Systems with Applications, 116, 147–160. https://doi.org/10.1016/j.eswa.2018.08.051.
 
Ashry, A., Alrahmawy, M., Rashad, M. (2020). Enhanced quantum inspired grey wolf optimizer for feature selection. International Journal of Intelligent Systems and Applications, 12, 8–17. https://doi.org/10.5815/ijisa.2020.03.02.
 
Barani, F., Mirhosseini, M., Nezamabadi-Pour, H. (2017). Application of binary quantum-inspired gravitational search algorithm in feature subset selection. Applied Intelligence, 47(2), 304–318. https://doi.org/10.1007/s10489-017-0894-3.
 
Battiti, R. (1994). Using mutual information for selecting features in supervised neural net learning. IEEE Transactions on Neural Networks, 5(4), 537–550. https://doi.org/10.1109/72.298224.
 
Chantar, H., Mafarja, M., Alsawalqah, H. (2020). Feature selection using binary grey wolf optimizer with elite-based crossover for Arabic text classification. Neural Computing and Applications, 32, 12201–12220. https://doi.org/10.1007/s00521-019-04368-6.
 
De Luca, A., Termini, S. (1972). A definition of a nonprobabilistic entropy in the setting of fuzzy sets theory. Information and Control, 20(4), 301–312. https://doi.org/10.1016/S0019-9958(72)90199-4.
 
Dimauro, G. (2011). Fourier transform in numeral recognition and signature verification. In: Wang, P.S.P. (Ed.), Pattern Recognition, Machine Intelligence and Biometrics. Springer, Berlin, Heidelberg, pp. 823–857. https://doi.org/10.1007/978-3-642-22407-2_31.
 
Fahmy, M.M.M. (2010). Online handwritten signature verification system based on DWT features extraction and neural network classification. Ain Shams Engineering Journal, 1(1), 59–70. https://doi.org/10.1016/j.asej.2010.09.007.
 
Fierrez-Aguilar, J., Nanni, L., Lopez-Peñalba, J., Ortega-Garcia, J., Maltoni, D. (2005). An on-line signature verification system based on fusion of local and global information. In: Kanade, T., Jain, A., Ratha, N.K. (Eds.), Audio- and Video-Based Biometric Person Authentication. Springer, Berlin, Heidelberg, pp. 523–532.
 
Ghosh, K.K., Singh, P.K., Hong, J., Geem, Z.W., Sarkar, R. (2020). Binary social mimic optimization algorithm with X-shaped transfer function for feature selection. IEEE Access, 8, 97890–97906. https://doi.org/10.1109/ACCESS.2020.2996611.
 
Hamed, H.N.A., Kasabov, N., Shamsuddin, S.M. (2009). Integrated feature selection and parameter optimization for evolving spiking neural networks using quantum inspired particle swarm optimization. In: International Conference of Soft Computing and Pattern Recognition, pp. 695–698. https://doi.org/10.1109/SoCPaR.2009.139.
 
Hancer, E. (2019). Differential evolution for feature selection: a fuzzy wrapper—filter approach. Soft Computing, 23(13), 5233–5248. https://doi.org/10.1007/s00500-018-3545-7.
 
Hancer, E. (2020). New filter approaches for feature selection using differential evolution and fuzzy rough set theory. Neural Computing and Applications, 32, 2929–2944. https://doi.org/10.1007/s00521-020-04744-7.
 
Hancer, E. (2021). An improved evolutionary wrapper-filter feature selection approach with a new initialisation scheme. Machine Learning, 1–24. https://doi.org/10.1007/s10994-021-05990-z.
 
Kohavi, R., John, G.H. (1997). Wrappers for feature subset selection. Artificial Intelligence, 97(1), 273–324. https://doi.org/10.1016/S0004-3702(97)00043-X.
 
Kıran, M.S., Gündüz, M. (2012). XOR-based artificial bee colony algorithm for binary optimization. Turkish Journal of Electrical Engineering and Computer Science, 21, 2307–2328. https://doi.org/10.3906/elk-1203-104.
 
Lam, C.F., Kamins, D. (1989). Signature recognition through spectral analysis. Pattern Recognition, 22(1), 39–44. https://doi.org/10.1016/0031-3203(89)90036-8.
 
Li, S.Z., Jain, A. (2015). Encyclopedia of Biometrics. Springer Publishing Company, Incorporated, Boston, MA. 148997489X.
 
Linden, J., Marquis, R., Bozza, S., Taroni, F. (2018). Dynamic signatures: a review of dynamic feature variation and forensic methodology. Forensic Science International, 291, 216–229. https://doi.org/10.1016/j.forsciint.2018.08.021.
 
Mafarja, M.M., Eleyan, D., Jaber, I., Hammouri, A., Mirjalili, S. (2017). Binary dragonfly algorithm for feature selection. In: International Conference on New Trends in Computing Sciences (ICTCS2017), pp. 12–17. https://doi.org/10.1109/ICTCS.2017.43.
 
Mailah, M., Han, L. (2012). Biometric signature verification using pen position, time, velocity and pressure parameters. Jurnal Teknologi, 48(1). https://doi.org/10.11113/jt.v48.218.
 
Mirjalili, S., Lewis, A. (2013). S-shaped versus V-shaped transfer functions for binary particle swarm optimization. Swarm and Evolutionary Computation, 9, 1–14. https://doi.org/10.1016/j.swevo.2012.09.002.
 
Nelson, W., Kishon, E. (1991). Use of dynamic features for signature verification. In: IEEE International Conference on Systems, Man, and Cybernetics, pp. 201–205. https://doi.org/10.1109/ICSMC.1991.169685.
 
Qasim, O.S., Algamal, Z.Y. (2020). Feature selection using different transfer functions for binary bat algorithm. International Journal of Mathematical, Engineering and Management Sciences, 5(4), 697–706. https://doi.org/10.33889/IJMEMS.2020.5.4.056.
 
Sakamoto, D., Morita, H., Ohishi, T., Komiya, Y., Matsumoto, T. (2001). On-line signature verification algorithm incorporating pen position, pen pressure and pen inclination trajectories. In: IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings, Vol. 2, pp. 993–996. https://doi.org/10.1109/ICASSP.2001.941084.
 
Samet, R., Hancer, E. (2012). A new approach to the reconstruction of contour lines extracted from topographic maps. Journal of Visual Communication and Image Representation, 23(4), 642–647. https://doi.org/10.1016/j.jvcir.2012.02.005.
 
Shannon, C.E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(3), 379–423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x.
 
Shi, Y., Eberhart, R.C. (1999). Empirical study of particle swarm optimization. In: Proceedings of the 1999 Congress on Evolutionary Computation (CEC99), Vol. 3, pp. 1945–1950. https://doi.org/10.1109/CEC.1999.785511.
 
Souza, V.L.F., Oliveira, A.L.I., Cruz, R.M.O., Sabourin, R. (2020). Improving BPSO-based feature selection applied to offline WI handwritten signature verification through overfitting control. In: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, GECCO ’20. Association for Computing Machinery, New York, NY, USA, pp. 69–70. https://doi.org/10.1145/3377929.3390038.
 
Witten, I.H., Frank, E. (2002). Data mining: practical machine learning tools and techniques with Java implementations. SIGMOD Record, 31(1), 76–77. https://doi.org/10.1145/507338.507355.
 
Wu, G., Wang, J., Zhang, Y., Jiang, S. (2018). A continuous identity authentication scheme based on physiological and behavioral characteristics. Sensors, 18(1), 179. https://doi.org/10.3390/s18010179.
 
Yanikoglu, B., Kholmatov, A. (2009). Online signature verification using fourier descriptors. Journal on Advances in Signal Processing, 2009, 260516. https://doi.org/10.1155/2009/260516.
 
Yeung, D.-Y., Chang, H., Xiong, Y., George, S., Kashi, R., Matsumoto, T., Rigoll, G. (2004). SVC2004: first international signature verification competition. In: Zhang, D., Jain, A.K. (Eds.), Biometric Authentication. Springer, Berlin, Heidelberg, pp. 16–22.
 
Yuan, X., Nie, H., Su, A., Wang, L., Yuan, Y. (2009). An improved binary particle swarm optimization for unit commitment problem. Expert Systems with Applications, 36(4), 8049–8055. https://doi.org/10.1016/j.eswa.2008.10.047.
 
Zouache, D., Ben Abdelaziz, F. (2018). A cooperative swarm intelligence algorithm based on quantum-inspired and rough sets for feature selection. Computers & Industrial Engineering, 115, 26–36. https://doi.org/10.1016/j.cie.2017.10.025.

Biographies

Hancer Emrah
https://orcid.org/0000-0002-3213-5191
ehancer@mehmetakif.edu.tr

E. Hancer received the BS degree in the Department of Mathematics and Computer Science from Cankaya University, Turkey, in 2009, and the MS and PhD degrees in Computer Engineering from Erciyes University, Turkey, in 2012 and 2016, respectively. During his PhD, he worked as a visiting researcher at Victoria University of Wellington in New Zealand between 2014 and 2015 for 1 year. He is currently an associate professor at the Department of Software Engineering, Mehmet Akif Ersoy University. In 2020 and 2021, he was selected among the top 2% of the world according to the article titled “Updated science-wide author databases of standardized citation indicators” published in PLOS Biology by Dr. John PA Ioannidis, K.W. Boyack, and J. Baas from Stanford University. His research interests include image analysis, clustering, dimensionality reduction, evolutionary computation and IoT authentication. Dr. Hancer has been serving as a reviewer for several international journals and conferences in the field, such as Applied Soft Computing, Pattern Recognition, IEEE CEC and IEEE SSCI.

Bardamova Marina
722bmb@gmail.com

M. Bardamova is a junior researcher at the Laboratory of Collection, Analysis and Control of Biological Signals at Tomsk State University of Control Systems and Radioelectronics (TUSUR). She graduated from the Faculty of Security of the same university in 2017. Currently, she is a senior instructor of the Department of Complex Information Security of Computer Systems. Her main research interests include data mining, fuzzy modelling and machine learning. She has authored and co-authored more than 10 journal and conference articles.

Hodashinsky Ilya
hodashn@gmail.com

I. Hodashinsky graduated from the Faculty of Control Systems, Novosibirsk Electrotechnical Institute, in 1975. He received the PhD degree in 1984, the Dr. Sc. degree in 2004 from Tomsk State University of Control Systems and Radioelectronics, Russia. He received the professor title at the 2011 and currently is a professor at Tomsk State University of Control Systems and Radioelectronics. His main research interests include the computational intelligence, fuzzy modelling, pattern recognition, knowledge discovery, and data mining. He is the author and co-author of over 170 journal and conference papers as well as technical articles. Prof. Hodashinsky is a member of IEEE, IEEE Computational Intelligence Society.

Sarin Konstantin
sks@security.tomsk.ru

K. Sarin recived Candidate of Sciences in Technology degree from Tomsk State University of Control Systems and Radioelectronics, Russian Federation, in 2016. He is currently an assistant professor at the Department of Complex Information Security of Computer Systems, Tomsk State University of Control Systems and Radioelectronics. His research interests include data mining, fuzzy systems, metaheuristic optimization and biometric authentication.

Slezkin Artem
saotom724@gmail.com

A. Slezkin is a certified specialist in the field of information security and automated systems (he received his specialist diploma in 2019 from Tomsk State University of Control Systems and Radioelectronics). He is also an experienced software developer, including software designed for auditing and information security. He is currently a postgraduate student at Tomsk State University of Control Systems and Radioelectronics and a junior researcher in the laboratory of extraction, analysis and management of biological signals at the Institute of Systems Integration and Security. His research interests include fuzzy systems, metaheuristic optimization algorithms, and feature selection.

Svetlakov Mikhail
svetlakov.m4@gmail.com

M. Svetlakov is a research associate at the Department of Complex Information Security of Computer Systems, Tomsk State University of Control Systems and Radioelectronics. M. S. received the qualification of Information Security Specialist from Tomsk State University of Control Systems and Radioelectronics, Russia, in 2019. His research interests include fuzzy systems, clustering, metaheuristics optimization, biometrics.


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Keywords
feature selection handwritten signature particle swarm optimization biometric authentication

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