Analysis of Nine Instance-Based Genetic Algorithm Classifiers Using Small Datasets

Authors

  • Hossin M. Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, 94300 Kota Samarahan, Sarawak, Malaysia.
  • Mahudin F. Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, 94300 Kota Samarahan, Sarawak, Malaysia.
  • Din I Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, 94300 Kota Samarahan, Sarawak, Malaysia.
  • Mat A.R. Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, 94300 Kota Samarahan, Sarawak, Malaysia.

Keywords:

Data Classification, Genetic Algorithm, Instance-Based Classifier,

Abstract

The application of genetic algorithm (GA) has emerged covering various areas including data classification. In data classification, most studies of GA were focused on the enhancement of GA and development of different types of GA classifiers. To the best of our knowledge, there is no study has been conducted to examine the influence of GA operators based on the size of data set towards training time and generalization ability. Therefore, this study develops and compares nine Instance-based genetic algorithm (IbGA) classifiers with different combinations of GA operators. The goal of this comparison is to examine and identify the best combination of GA operators which have performed better on generalization ability and training time efficiency. Nineteen benchmark data sets were used in this study. The non-parametric statistical tests were applied to justify the comparison results. The statistical tests suggest that the combination of roulette wheel selection and uniform crossover operator is the best combination of IbGA model although the training time is a bit lengthier than compared to other IbGA models.

References

S. Garcia, J. Derrac, J. R. Cano, and F. Herrera, “Prototype Selection for Nearest Neighbor Classification: Taxonomy and Empirical Study,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 3, pp. 417-435, 2012.

L. Yang, D. H. Widyantoro, T. Ioerger, and J. Yen, “An entropy-based adaptive genetic algorithm for learning classification rules,” In Evolutionary Computation, 2001, Proceedings of the 2001 Congress on, vol. 2, pp. 790-796, 2001. IEEE

J. Liu, H. Iba, and M. Ishizuka, “Selecting informative genes with parallel genetic algorithms in tissue classification,” Genome Informatics, vol. 12, pp. 14-23, 2001.

P. Vivekanandan, M. Rajalakshmi, and R. Nedunchezhian, “An intelligent genetic algorithm for mining classification rules in large datasets,” Computing and Informatics, vol. 32, no. 1, pp. 1-22, 2013.

L. I. Kuncheva and J. C. Bezdek, “Nearest prototype classification: clustering, genetic algorithms, or random search?,” Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, vol. 28, no. 1, pp. 160-164, 1998.

H. Ishibuchi and T. Nakashima, “Multi-objective pattern and feature selection by a genetic algorithm,” In GECCO, pp. 1069, 2000.

O. Abdoun and J. Abouchabaka, “A comparative study of adaptive crossover operators for genetic algorithms to resolve the traveling salesman problem,” arXiv preprint arXiv:1203.3097, 2012.

A. V. Andrade, L. D. Errico, A. L. L. D. Aquino, L. P. D. Assis, and C. H. N. D. R. Barbosa, “Analysis of selection and crossover methods used by genetic algorithm-based heuristic to solve the lsp allocation problem in mpls networks under capacity constraints,” 2008.

N. García-Pedrajas, J. A. R. Del Castillo, and D. Ortiz-Boyer, “A cooperative coevolutionary algorithm for instance selection for instance-based learning,” Machine Learning, vol. 78, no. 3, pp. 381- 420, 2010.

J. Magalhães-Mendes, “A comparative study of crossover operators for genetic algorithms to solve the job shop scheduling problem,” WSEAS transactions on computers, vol. 12, no. 4, pp. 164-173, 2013.

J. Zhong, X. Hu, M. Gu, and J. Zhang, “Comparison of performance between different selection strategies on simple genetic algorithms,” In Computational Intelligence for Modelling, Control and Automation, 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, International Conference on, vol. 2, pp. 1115-1121, Nov. 2005. IEEE.

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Published

2017-12-07

How to Cite

M., H., F., M., I, D., & A.R., M. (2017). Analysis of Nine Instance-Based Genetic Algorithm Classifiers Using Small Datasets. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(3-11), 67–71. Retrieved from https://jtec.utem.edu.my/jtec/article/view/3186

Issue

Vol. 9 No. 3-11: Computing Technology Towards A Sustainable Society

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Articles

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