Abstract—Dimensionality reduction of a feature set is a usual pre-processing step used for classification applications to improve their accuracy with a small and appropriate feature subset. In this article, a new hybrid of ant colony optimization (ACO) and genetic algorithm (GAs) as a feature selector, and support vector machine as a classifier are integrated effectively. Based on the combination of the fast global search ability of GA and the positive feedback mechanism of ACO, a novel algorithm was proposed in the domain of feature selection. Experiments show that the proposed feature selection can achieve better performance than that the normal GA and ACO does. We tested this method on the extracted features of ten common Persian fonts. The result shows it has affected slightly better on the performance. Furthermore, number of features decreased to almost half of the original feature number after this pre-processing step.
Index Terms—Feature subset selection, ant colony optimization, genetic algorithm, persian font recognition.
Maryam Bahojb Imani is with Computer and Information Technology Engineering Department, Amirkabir University of Technology, Tehran, Iran (e-mail: firstname.lastname@example.org). Tahereh Pourhabibi is with the Computer Engineering Department, Alzahra University, Tehran, Iran (e-mail: email@example.com).
Mohammad reza Keyvanpour is with Computer Engineering Department, Alzahra University, Tehran, Iran (e-mail: Keyvanpour@alzahra.ac.ir).
R. Azmi is with Computer Engineering Department, Alzahra university, Tehran, Iran (e-mail: firstname.lastname@example.org).
Cite: Maryam Bahojb Imani, Tahereh Pourhabibi, Mohammad Reza Keyvanpour, and Reza Azmi, "A New Feature Selection Method Based on Ant Colony and Genetic Algorithm on Persian Font Recognition," International Journal of Machine Learning and Computing vol. 2, no. 3, pp. 278-282, 2012.