Application of Imperialist Competitive Algorithm for Feature Selection: A Case Study on Bulk Rice Classification
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10.5120/5068-7485 |
S J MousaviRad, Akhlaghian F Tab and K Mollazade. Article: Application of Imperialist Competitive Algorithm for Feature Selection: A Case Study on Bulk Rice Classification. International Journal of Computer Applications 40(16):41-48, February 2012. Full text available. BibTeX
@article{key:article,
author = {S. J. MousaviRad and F. Akhlaghian Tab and K. Mollazade},
title = {Article: Application of Imperialist Competitive Algorithm for Feature Selection: A Case Study on Bulk Rice Classification},
journal = {International Journal of Computer Applications},
year = {2012},
volume = {40},
number = {16},
pages = {41-48},
month = {February},
note = {Full text available}
}
Abstract
Feature selection plays an important role in pattern recognition. The better selection of a feature set usually results the better performance in a classification problem. This work tries to select the best feature set for classification of rice varieties based on image of bulk samples using imperialist competition algorithm. Imperialist competition algorithm is a new evolutionary optimization method that is inspired by imperialist competition. Results showed the feature set selected by the imperialist competition algorithm provide the better classification performance compared to that obtained by genetic algorithm technique
References
- X. Wang,j. Yang, X.Teng, W.Xia, R.Jensen, 2007, Feature selection based on rough sets and particle swarm optimization, Pattern Recognition Letters, vol. 28, pp. 459-471.
- H. Uguz, 2011, A two-stage feature selection method for text categorization by using information gain, principal component analysis and genetic algorithm, Knowledge-Based Systems, vol. 24, pp. 1024-1032
- A. A. Ahmed, 2005, Feature subset selection using ant colony optimization, International Journal of Computational Intelligence, vol. 2, pp. 53-58.
- E. Atashpaz-Gargari and C. Lucas, 2007, Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition, pp. 4661-4667.
- Z.Liu,F. Cheng,, Y.Ying, X.Rao, 2005, Identification of rice seed varieties using neural network, Journal of Zhejiang University Science B, vol. 6, pp. 1095-1100.
- S.J Mousavi Rad, F. Akhlaghian, K.Mollazade, 2011, classification of rice varieties using optimal color and texture features and BP Neural Network, The 7th Iranian Conference on Machine Vision and Image Processing, Iran University of Science and Technology.
- B. Verma, 2010, Image processing techniques for grading & classification of rice, International conferences on computer and communication technology, pp. 220-223.
- C. Zheng, D.W. Sun, L. Zheng, 2006, Recent applications of image texture for evaluation of food qualities-a review, Trends in Food Science & Technology, vol. 17, pp. 113-128.
- R. M. Haralick, k.shanmugam, I.H. Dinstein , 1973, Textural features for image classification, Systems, Man and Cybernetics, IEEE Transactions on, vol. 3, pp. 610-621.
- A. Mucherino,P.J. papajorgji, P.M. Pardalos, 2009, Data mining in agriculture vol. 34, Springer Verlag.