Classification Process using Hybrid model of Rough Neural Networks and Gene Expression Programming

Yasser Fouad

Abstract


The paper will introduce a new model of rough neural networks based on learning using gene expression programming for classification support. The Objective of gene expression programming rough neural networks approach is to obtain new classified data with minimum error in training and testing process. Starting point of gene expression programming rough neural networks approach is an information system and the output from this approach is a structure of rough neural networks which is including the weights and thresholds.


Keywords


Rough Sets; Gene Expression Programming; Rough Neural Networks; Classification

Full Text:

PDF

References


Aboul Ella Hassanien (2006) Rough Neural Intelligent Approach for Image Classification: A Case of Patients with Suspected Breast Cancer. International Journal of Hybrid Intelligent System, IOS press, 2006 (to appear).

Coaquira F., and Acuna, E. (2007). Applications of Rough sets theory to data preprocessing in Knowledge Discovery. To appear in Proceedings of the conference of Machine learning and data analysis to be held October 2007 at UC Berkeley, California

Ferreira, C. (2002) Genetic representation and genetic neutrality in gene expression programming. Advances in Complex Systems, 5 (4): 389-408.

Ferreira, C. (2003) Function finding and the creation of numerical constants in gene expression programming. In J. M. Benitez,O. Cordon, F. Hoffmann, and R. Roy, eds., Advances in Soft Computing: Engineering Design and Manufacturing, pp.257-266, Springer-Verlag.

Hassan Y-F. (2014) New model of rough neural networks based on rough dependency to adapt cellular automata model, Kokull Journal, Vol. 64, No. 5.

Ferreira, C. ( 2001) Gene expression programming: Anew adaptive algorithm for solving problems. Complex Systems, 13(2): 87-129.

Lingras P. J. (1996) Rough neural networks. In: Proc. Of the 6th Int. Conf. on In-formation Processing and Management of Uncertainty in Knowledge-based Systems (IPMU96), Granada, Spain, pp.1445-1450

Nordin, M.A.R., Yazid, M.M.S., Aziz, A.,Osman, A.M.T.: DNA Sequence Database Classification and Reduction: Rough Sets Theory Approach. Proc. of 2nd International Conference on Informatics, pp. 41--47 (2007)

Pal S.K. Polkowski S.K. and Skowron A. (Eds.) (2002) Rough-Neuro Computing:Techniques for Computing with Words. Berlin: Springer-Verlag.

Peters J.F. Liting H. and Ramanna S. (2001) Rough Neural Computing in Signal Analysis. Computational Intelligence vol. 17, no.3: pp. 493-513.

Shyng, J-Y., Wang, F-K., Tzeng, G-H., Wu, K-S.(2007) Rough Set Theory in Analyzing the Attributes of Combination Values for the Insurance Market. Journal of Expert Systems with Application. vol. 32, pp. 56—64.

J. R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection, (MIT Press, Cambridge, MA, 1992)

Rosetta, A Rough Set Toolkit for Analysis of Data, web address http://www.lcb.uu.se/tools/rosetta/index.php. May 12, 2009

GeneXproTools application, web address: http://www.gepsoft.com/, Released February 19, 2014

NeuroIntelligence 2.2 application, web address: http://www.forex-warez.com/Free%20Download/rapidshare/Software/Alyuda%20NeuroIntelligence%202.2%20Build%20577%20(alyuda.com)/ 2005

Jan G. Bazan, Hung Son Nguyen, Sinh Hoa Nguyen, Rough Set Algorithms in Classification Problem (2000)

Kursat. Z, Novruz. A, Nihat. A.: Genetic algorithm and rough sets based hybrid approach for reduction of input attributes in medical systems “. International journal of innovative computing, information and control. Vol.9, number 7.ICIC©203 ISSN1349-4198 (July 2013)

J. H. Holland, Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, second edition (MIT Press, 1992)

S. Wang: Algorithms for solving the reducts problem in rough sets. Master's Projects. Paper 285.http://scholarworks.sjsu.edu/etd_projects/285 (2012)

UCI Machine Learning Repository, Center for Machine Learning and Intelligent Systems, http://archive.ics.uci.edu/ml/datasets/Iris (1988)

UCI Machine Learning Repository, Center for Machine Learning and Intelligent Systems,https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Original%29 (1992).

Goldberg D. E., Genetic Algorithms in Search, Optimization and Machine Learning, Addison Wesley, 1989.




DOI: http://dx.doi.org/10.21535%2Fijrm.v2i4.860

Refbacks

  • There are currently no refbacks.




Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.