The week's pick
Reseach Article
A Novel Approach to Classificatory Problem using Grammatical Evolution based Hybrid Algorithm
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 1 - Number 28 |
| Year of Publication: 2010 |
| Authors: Rahul Kala, Anupam Shukla, Ritu Tiwari |
10.5120/509-826
|
Rahul Kala, Anupam Shukla, Ritu Tiwari . A Novel Approach to Classificatory Problem using Grammatical Evolution based Hybrid Algorithm. International Journal of Computer Applications. 1, 28 ( February 2010), 61-68. DOI=10.5120/509-826
Abstract
It is now well known that most of the problems are classificatory in nature eg Face Recognition, Speaker Recognition, Word Recognition, etc. In this paper we propose a new model for these classificatory problems. This model is based on the Grammatical Evolution of the Genetic Algorithms. The model that we propose here is a type of fuzzy logic inference system model. Rules are in form of a collection of points representing every class. The separation between the unknown input and these representative points determine the degree of belongingness of the unknown input to the specific class being considered. Multiple contributions from same classes are simply added together. The training data set is used for the purpose of generating the initial set of configurations of this fuzzy model. The fuzzy functions are parameterized by adding fuzzy parameters, like any neuro-fuzzy model. These parameters are trained by a valedictory data set using a training algorithm. The performance of the system over the training and valedictory data set serve as the fitness function. Variable mutation rate is applied. We tested the effectiveness of the algorithm over the picture learning problem and received better results than any of the present algorithms.
References
- Adams, Rod, Calcraft, Lee & Davey, Neil, Using a genetic algorithm to investigate efficient connectivity in associative memories, Neurocomputing, doi:10.1016/j.neucom.2008.05.017
- Amin, Md. Faijul & Murase, Kazuyuki, Single-layered complex-valued neural network for real-valued classification problems, Neurocomputing, doi:10.1016/j.neucom.2008.04.006
- Ang, J. H., Tan, K.C. & Mamun & A Al, Training neural networks for classification using growth probability-based evolution, Neurocomputing, doi:10.1016/j.neucom.2007.10.011
- Chaudhuri, B.B. & Bhattacharya U, Efficient training and improved performance of multilayer perceptron in pattern classification, Neurocomputing 34 (2000) 11-27
- Draghici, S., A neural network based artificial vision system for license plate recognition, International Journal of Network Security, International Journal of Neural Systems, Vol. 8, No. 1, 1997
- Er, M.J., Zhou, Y, A novel framework for automatic generation of fuzzy neural networks, Neurocomputing, Vol 21, Issue 4-6, pp 584-591, Jan 2008
- Estudillo, F.J. Martinez, Martinez, C. Hervas, Gutierrez, P.A. & Estudillo, A.C. Martinez, Evolutionary product-unit neural networks classifiers, Neurocomputing, doi:10.1016/j.neucom.2007.11.019
- Gernot, A., Fink, P. & Thomas, Unsupervised Estimation of Writing Style Models for Improved Unconstrained Off-line Handwriting Recognition
- Graves, A., Fernandez, S., Liwicki, M., Bunke, H. & Schmidhuber, J., Unconstrained Online Handwriting Recognition with Recurrent Neural Networks, Advances in Neural Information Processing Systems, 2008
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