The GSA Algorithm at Two Methods of Feature Selection and Weighting Features to Improve the Recognition Rate of Persian Handwrite Digits with Fuzzy Classifier
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10.5120/18555-7137 |
Najme Ghanbari, Sedighe Ghanbari and Somayeh B Mousavi. Article: The GSA Algorithm at Two Methods of Feature Selection and Weighting Features to Improve the Recognition Rate of Persian Handwrite Digits with Fuzzy Classifier. International Journal of Computer Applications 106(10):11-15, November 2014. Full text available. BibTeX
@article{key:article,
author = {Najme Ghanbari and Sedighe Ghanbari and B. Somayeh Mousavi},
title = {Article: The GSA Algorithm at Two Methods of Feature Selection and Weighting Features to Improve the Recognition Rate of Persian Handwrite Digits with Fuzzy Classifier},
journal = {International Journal of Computer Applications},
year = {2014},
volume = {106},
number = {10},
pages = {11-15},
month = {November},
note = {Full text available}
}
Abstract
In this paper, using Gravitational search algorithm or GSA recognition rate of Persian handwritten digits can be improved. Two methods have been proposed to improve the recognition rate. in the first method, with using of version binary of Gravitational search algorithm or BGSA, we choose optimal features among the overall features extracted. Finding the best feature sets from entire extracted features, not only the number of features and computational burden will be reduced but also recognition rate will be significantly improved. Also In this paper, real version of GSA or RGSA has been used in different way (secondary methods) to improve recognition rate. In this method, instead of choosing some of the features, one random weight has been assigned to each feature. Indeed, feature vector has been multiplied in weight vector to obtain a new feature vector. This Weight vector is obtained with RGSA. After several iterations, RGSA algorithm determines Weight set of features so that Classification accuracy increases. In this paper, the fuzzy classifier is used for classification. Fitness function in BGSA and RGSA algorithms is the number of fuzzy classifier errors and the aim is to make this value minimum. The obtained results confirmed that these algorithms have proper performance.
References
- Johari majd. V, razavi. S. m, fuzzy recognition of Persian handwritten digits, First Iranian Conference on Machine Vision and Image Processing, pp. 144 -151, 2000.
- Razavi. S. m, sadoghi yazdi. H, kabir. E, feature selection for Persian handwritten digits recognition using of Genetics algorithm, Seventh Annual Conference of Computer Society of Iran , pp. 285-292, 2001.
- Rashedi,esmat, Nezamabadi Pour,H, Saryazdi,S. binary Gravitational search algorithm, the first member of congress on fuzzy and intelligent systems,mashhad, 2007.
- Nafisi. H. r, kabir. E, Persian handwritten digits recognition, Second Iranian Conference on Electrical Engineering, pp. 295- 304,1994.
- Darwish,A. , Kabir,E. , Khosravi,H. , application of shape match in Persian handwritten digits recognition, Journal of Jihad, No. 22, 2005.
- Nahvi,M. , Rafiei,M. ,Ebrahim Pour,R. ,Kabir,E. ,The combination of the two-class classifiers for recognition of Persian handwritten figures, Sixteenth Conference of Electrical Engineering, Iran, 2008.
- Tohidi,H. , Nezamabadi Pour,H,Saryazdi,S. , feature selection algorithm using binary population of ants", First International Conference on Fuzzy and Intelligent Systems, Ferdowsi University of Mashhad, 2005.
- Khosravi,H. , Kabir,E. , The letters and numbers recognition of Persian handwritten in the national exam registration forms , Fifteen of the first page master's thesis,2006.
- H. Khosravi, E. Kabir," Introducing a very large dataset of handwritten Farsi digits and a study on their varienties", Pattern recognition letters 28, 1133-1141, 2007.
- E. Rashedi, H. Nezamabadi-pour, S. Saryazdi, "GSA: A Gravitational Search Algorithm", Information Sciences 179, 22322248, 2009.
- Y. Qi, J. Tian, R. W. Dai, "Fingerprint classification system with feedback mechanism based on genetic algorithm", IEEE, 1998.
- J. G. Smith, T. C. Fogarty and I. R. Johnson, "Genetic selection of feature for clustering and classification" , 1994.