论文信息 - Application of Imperialist Competitive Algorithm for Feature Selection: A Case Study on Bulk Rice Classification

Application of Imperialist Competitive Algorithm for Feature Selection: A Case Study on Bulk Rice Classification

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

[1] Fardin Akhlaghian Tab,et al. Classification of Rice Varieties Using Optimal Color and Texture Features and BP Neural Networks , 2011, 2011 7th Iranian Conference on Machine Vision and Image Processing.

[2] Robert M. Haralick,et al. Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[3] Bhupinder Verma,et al. Image processing techniques for grading & classification of rice , 2010, 2010 International Conference on Computer and Communication Technology (ICCCT).

[4] Panos M. Pardalos,et al. Data Mining in Agriculture , 2008 .

[5] Xiangyang Wang,et al. Feature selection based on rough sets and particle swarm optimization , 2007, Pattern Recognit. Lett..

[6] Caro Lucas,et al. Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition , 2007, 2007 IEEE Congress on Evolutionary Computation.

[7] Da-Wen Sun,et al. Recent applications of image texture for evaluation of food qualities—a review , 2006 .

[8] F. Cheng,et al. Identification of rice seed varieties using neural network. , 2005, Journal of Zhejiang University. Science. B.

[9] Ahmed Al-Ani,et al. Feature Subset Selection Using Ant Colony Optimization , 2008 .

[10] Harun Uguz,et al. A two-stage feature selection method for text categorization by using information gain, principal component analysis and genetic algorithm , 2011, Knowl. Based Syst..