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Journal of Artificial Intelligence and Metaheuristics
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Title

Evaluating the Effect of Optimized Voting Using Hybrid Particle Swarm and Grey Wolf Algorithm on the Classification of the Zoo Dataset

  Doaa S. Khafaga 1 * ,   Hussein Alkattan 2 ,   Alhumaima A. Subhi 3

1  Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
    (dskhafga@pnu.edu.sa)

2  Department of System Programming South Ural State University; pr. Lenina 76, Chelyabinsk, 454080 Russia
    (alkattan.hussein92@gmail.com)

3  Electronic and Computer Center, University of Diyala, Baqubah MJJ2+R9G, Iraq
    (alhumaimaali@uodiyala.edu.iq)


Doi   :   https://doi.org/10.54216/JAIM.020101

Received: April 06, 2022 Accepted: October 08, 2022

Abstract :

When there are numerous possible solutions for a given class in a given problem, majority voting or plurality voting is typically employed. One common technique for improving classification accuracy is bagging, which involves training many classifiers on slightly different datasets and then voting on the combined results. In this research, we examine how alternative voting procedures affect the efficiency of two distinct classification algorithms applied to datasets of varying complexity. Despite the increased computing cost associated with determining preference order, the results show that the single transferable vote can be a suitable alternative to plurality voting.

Keywords :

Zoo data; Voting classifier; Support vector manchines; Neural networks; Decision trees.

References :

[1] L. Breiman, ―Bagging predictors‖, Machine Learning, vol.24, pp. 123- 140, 1996.

[2] R.E. Schapire, Y. Singer ―Improved boosting algorithms using confidence-rated predictions‖,

Machine Learning, vol. 37, pp. 297-336, 1999.

[3] M. Van Erp, L. Vuurpijl, L. Schomaker, ―An overview and comparison of voting methods for

pattern recognition‖, IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in

Handwriting Recognition, pp. 195-200, 2002.

[4] K.T. Leung, D.S. Parker, ―Empirical comparisons of various voting methods in bagging‖, KDD '03

Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data

mining, pp. 595-600, 2003

[5] C. Cohagan, J.W. Grzymala-Busse, Z.S. Hippe, ―A comparison of three voting methods for bagging

with the MLEM2 algorithm‖, IDEAL'10 Proceedings of the 11th international conference on

Intelligent data engineering and automated learning, pp. 118-125, 2010.

[6] K. Machová, F. Barčák, P. Bednár, ―A bagging method using decision trees in the role of base

classifiers‖, Acta Polytechnica Hungarica, vol.3, pp. 121-132, April 2006.

[7] N. Abdel Samee, E. M. El-Kenawy, G. Atteia, M. M. Jamjoom, A. Ibrahim et al., "Metaheuristic

optimization through deep learning classification of covid-19 in chest x-ray images," Computers,

Materials & Continua, vol. 73, no.2, pp. 4193–4210, 2022.

[8] A. A. Abdelhamid and S. R. Alotaibi, "Optimized two-level ensemble model for predicting the

parameters of metamaterial antenna," Computers, Materials & Continua, vol. 73, no.1, pp. 917–933,

2022.

[9] R.D. Kulkarni, ―Using esemble methods for improving classification of the KDD CUP’99 data set‖,

IOSR Journal of Computer Engineering (IOSR-JCE), vol. 16, pp. 57-61, 2014.

[10] A. A. Abdelhamid and S. R. Alotaibi, "Robust prediction of the bandwidth of metamaterial antenna

using deep learning," Computers, Materials & Continua, vol. 72, no.2, pp. 2305–2321, 2022.

[11] Y. Zhang, H. Zhang, J. Cai, B. Yang, ―A weighted voting classifier based on differential evolution‖,

Abstract and Applied Analysis, 2014.

[12] T. Saito, ―Theoretical Model: Condorcet’s Jury Theorem, Part 1‖, Wolfram Demonstrations Project,

http://demonstrations.wolfram.com/TheoreticalModelCondorcetsJuryTheoremPart1, 2017.

[13] F. Leon, C.G. Piuleac, S. Curteanu, I. Poulios, ―Instance-based regression with missing data applied

to a photocatalytic oxidation process‖, Central European Journal of Chemistry, vol. 10, no. 4, pp.

1149-1156, 2012.

[14] F. Leon, C. Lisa, S. Curteanu, ―Prediction of the liquid crystalline property using different

classification methods‖, Molecular Crystals and Liquid Crystals, vol. 518, pp. 129-148, 2010.

[9] F. Leon, S. Curteanu, C. Lisa, N. Hurduc, ―Machine learning methods used to predict the liquidcrystalline

behavior of some copolyethers‖, Molecular Crystals and Liquid Crystals, vol. 469, pp. 1-

22, Taylor and Francis Group, USA, 2007, DOI: 10.1080/15421400701431232.

[10] M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann and I.H. Witten, ―The WEKA data

mining software: An Update―, ACM SIGKDD Explorations, vol. 11, no. 1, pp. 10–18, 2009.

[11] R.N. Shepard, ―Toward a universal law of generalization for psychological science‖, Science, vol.

237, pp. 1317–1323, 1987.

[12] K.Q. Weinberger and L.K. Saul, ―Distance metric learning for large margin nearest neighbor

classification‖, Journal of Machine Learning Research, vol. 10, pp. 207–244, 2009.

[13] F. Leon, S. Curteanu, ―Large margin nearest neighbour regression using different optimization

techniques‖, Journal of Intelligent & Fuzzy Systems, vol. 32, pp. 1321-1332, 2017.

[14] Abdelhamid, A.A.; El-Kenawy, E.-S.M.; Khodadadi, N.; Mirjalili, S.; Khafaga, D.S.; Alharbi, A.H.;

Ibrahim, A.; Eid, M.M.; Saber, M. Classification of Monkeypox Images Based on Transfer Learning

and the Al-Biruni Earth Radius Optimization Algorithm. Mathematics 2022, 10, 3614.

[15] Eid, M.M.; El-Kenawy, E.-S.M.; Khodadadi, N.; Mirjalili, S.; Khodadadi, E.; Abotaleb, M.; et al.,

Meta-Heuristic Optimization of LSTM-Based Deep Network for Boosting the Prediction of

Monkeypox Cases. Mathematics 2022, 10, 3845.

[16] R. A. Fisher, ―The use of multiple measurements in taxonomic problems‖, Annual Eugenics, vol. 7,

part II, pp. 179-188, 1936.

[17] R. O. Duda, P. E. Hart,‖ Pattern classification and scene analysis‖, John Wiley & Sons, p. 218,1973.

[18] I.W. Evett and E.J. Spiehler, ―Rule induction in forensic science‖, Central Research Establishment.

Home Office Forensic Science Service. Aldermaston, Reading, Berkshire RG7 4PN.

[19] P. W. Frey and D. J. Slate, "Letter recognition using holland-style adaptive classifiers", Machine

Learning, vol 6, 1991.

[20] D. Sami Khafaga, A. Ali Alhussan, E. M. El-kenawy, A. Ibrahim, S. H. Abd Elkhalik et al.,

"Improved prediction of metamaterial antenna bandwidth using adaptive optimization of lstm,"

Computers, Materials & Continua, vol. 73, no.1, pp. 865–881, 2022.


Cite this Article as :
Style #
MLA Doaa S. Khafaga, Hussein Alkattan, Alhumaima A. Subhi. "Evaluating the Effect of Optimized Voting Using Hybrid Particle Swarm and Grey Wolf Algorithm on the Classification of the Zoo Dataset." Journal of Artificial Intelligence and Metaheuristics, Vol. 2, No. 1, 2022 ,PP. 08-15 (Doi   :  https://doi.org/10.54216/JAIM.020101)
APA Doaa S. Khafaga, Hussein Alkattan, Alhumaima A. Subhi. (2022). Evaluating the Effect of Optimized Voting Using Hybrid Particle Swarm and Grey Wolf Algorithm on the Classification of the Zoo Dataset. Journal of Journal of Artificial Intelligence and Metaheuristics, 2 ( 1 ), 08-15 (Doi   :  https://doi.org/10.54216/JAIM.020101)
Chicago Doaa S. Khafaga, Hussein Alkattan, Alhumaima A. Subhi. "Evaluating the Effect of Optimized Voting Using Hybrid Particle Swarm and Grey Wolf Algorithm on the Classification of the Zoo Dataset." Journal of Journal of Artificial Intelligence and Metaheuristics, 2 no. 1 (2022): 08-15 (Doi   :  https://doi.org/10.54216/JAIM.020101)
Harvard Doaa S. Khafaga, Hussein Alkattan, Alhumaima A. Subhi. (2022). Evaluating the Effect of Optimized Voting Using Hybrid Particle Swarm and Grey Wolf Algorithm on the Classification of the Zoo Dataset. Journal of Journal of Artificial Intelligence and Metaheuristics, 2 ( 1 ), 08-15 (Doi   :  https://doi.org/10.54216/JAIM.020101)
Vancouver Doaa S. Khafaga, Hussein Alkattan, Alhumaima A. Subhi. Evaluating the Effect of Optimized Voting Using Hybrid Particle Swarm and Grey Wolf Algorithm on the Classification of the Zoo Dataset. Journal of Journal of Artificial Intelligence and Metaheuristics, (2022); 2 ( 1 ): 08-15 (Doi   :  https://doi.org/10.54216/JAIM.020101)
IEEE Doaa S. Khafaga, Hussein Alkattan, Alhumaima A. Subhi, Evaluating the Effect of Optimized Voting Using Hybrid Particle Swarm and Grey Wolf Algorithm on the Classification of the Zoo Dataset, Journal of Journal of Artificial Intelligence and Metaheuristics, Vol. 2 , No. 1 , (2022) : 08-15 (Doi   :  https://doi.org/10.54216/JAIM.020101)