Abstract
A meta-heuristic called Co-Operation of Biology Related Algorithms (COBRA) is applied to the design of a fuzzy rule-based classifier. The basic idea consists in the representation of a fuzzy classifier rule base as a binary string and the use of the binary modification of COBRA with a biogeography migration operator for the selection of the fuzzy classifier rule base. The parameters of the membership functions of the fuzzy classifier, represented as a string of real-valued variables, are adjusted with the original version of COBRA. Two medical diagnostic problems are solved with this approach. Experiments showed that the modification of COBRA demonstrates high performance and reliability in spite of the complexity of the optimization problems solved. Fuzzy classifiers developed in this way outperform many alternative methods at the given classification problems. The workability and usefulness of the proposed algorithms are confirmed.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Angelov, P., Zhou, X., Klawonn, F.: Evolving fuzzy rule-based classifiers. In: IEEE Symposium on Computational Intelligence in Image and Signal Processing, pp. 220–225 (2007)
Sanz, J., Fernandez, A., Bustince, H., Herrera, F.: A genetic tuning to improve the performance of fuzzy rule-based classification systems with interval-valued fuzzy sets: degree of ignorance and lateral position. Int. J. Approximate Reasoning 55(6), 751–766 (2011)
Akhmedova, Sh., Semenkin, E.: Co-operation of biology related algorithms. In: IEEE Congress on Evolutionary Computation, pp. 2207–2214 (2013)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)
Yang, Ch., Tu, X., Chen, J.: Algorithm of marriage in honey bees optimization based on the wolf pack search. In: International Conference on Intelligent Pervasive Computing, pp. 462–467 (2007)
Yang, X.S.: Firefly algorithms for multimodal optimization. In: 5th Symposium on Stochastic Algorithms, Foundations and Applications, pp. 169–178 (2009)
Yang, X.S., Deb, S.: Cuckoo search via levy flights. In: World Congress on Nature and Biologically Inspired Computing, pp. 210–214. IEEE Publications, USA (2009)
Yang, X.-S.: A new metaheuristic bat-inspired algorithm. In: González, J.R., Pelta, D.A., Cruz, C., Terrazas, G., Krasnogor, N. (eds.) NICSO 2010. SCI, vol. 284, pp. 65–74. Springer, Heidelberg (2010)
Akhmedova, Sh., Semenkin, E.: Co-operation of biology related algorithms meta-heuristic in ANN-based classifiers design. In: IEEE World Congress on Computational Intelligence, pp. 867–873 (2014)
Kennedy, J., Eberhart, R.: A discrete binary version of the particle swarm algorithm. In: World Multiconference on Systemics, Cybernetics and Informatics, pp. 4104–4109 (1997)
Simon, D.: Biogeography-based optimization. IEEE Trans. Evol. Comput. 12(6), 702–713 (2008)
Simon, D.: A probabilistic analysis of a simplified biogeography-based optimization algorithm. Evol. Comput. 19(2), 167–188 (2011)
Molga, M., Smutnicki, Cz.: Test Functions for Optimization Need (2005)
Marcano-Cedeno, A., Quintanilla-Dominguez, J., Andina, D.: WBCD breast cancer database classification applying artificial metaplasticity neural network. Expert Syst. Appl. 38(8), 9573–9579 (2011)
Temurtas, H., Yumusak, N., Temurtas, F.: A comparative study on diabetes disease diagnosis using neural networks. Expert Syst. Appl. 36(4), 8610–8615 (2009)
Acknowledgement
Research is performed with the financial support of the Ministry of Education and Science of the Russian Federation within the State Assignment for the Siberian State Aerospace University, project 2.1889.2014/K.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Akhmedova, S., Semenkin, E., Stanovov, V. (2016). Fuzzy Rule-Based Classifier Design with Co-operation of Biology Related Algorithms. In: Tan, Y., Shi, Y., Li, L. (eds) Advances in Swarm Intelligence. ICSI 2016. Lecture Notes in Computer Science(), vol 9713. Springer, Cham. https://doi.org/10.1007/978-3-319-41009-8_21
Download citation
DOI: https://doi.org/10.1007/978-3-319-41009-8_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-41008-1
Online ISBN: 978-3-319-41009-8
eBook Packages: Computer ScienceComputer Science (R0)