TY - JOUR TI - Handwritten Digits Recognition Using an Ensemble Technique Based on the Firefly Algorithm JO - Journal of Information Systems and Telecommunication (JIST) JA - Iranian Academic Center for Education,Culture and Research LA - en SN - 2322-1437 AU - Azar Mahmoodzadeh AU - Hamed Agahi AU - Marzieh Salehi AD - Islamic Azad University Shiraz AD - Islamic Azad University Shiraz AD - Islamic Azad University,Shiraz Branch, Iran Y1 - 2019 PY - 2019 VL _ 23 IS - 1 SP - 136 EP - 149 KW - Optical character recognition KW - KW - feature selection KW - KW - multi-objective genetic algorithm KW - KW - classifiers ensemble KW - KW - evolutionary firefly algorithm DO - 10.7508/jist.2018.03.003 N2 - This paper develops a multi-step procedure for classifying Farsi handwritten digits using a combination of classifiers. Generally, the technique relies on extracting a set of characteristics from handwritten samples, training multiple classifiers to learn to discriminate between digits, and finally combining the classifiers to enhance the overall system performance. First, a pre-processing course is performed to prepare the images for the main steps. Then three structural and statistical characteristics are extracted which include several features, among which a multi-objective genetic algorithm selects those more effective ones in order to reduce the computational complexity of the classification step. For the base classification, a decision tree (DT), an artificial neural networks (ANN) and a k-nearest neighbor (KNN) models are employed. Finally, the outcomes of the classifiers are fed into a classifier ensemble system to make the final decision. This hybrid system assigns different weights for each class selected by each classifier. These voting weights are adjusted by a metaheuristic firefly algorithm which optimizes the accuracy of the overall system. The performance of the implemented approach on the standard HODA dataset is compared with the base classifiers and some state-of-the-art methods. Evaluation of the proposed technique demonstrates that the proposed hybrid system attains high performance indices including accuracy of 98.88% with only eleven features. UR - http://rimag.ir/fa/Article/15183 L1 - http://rimag.ir/fa/Article/Download/15183 TY -JOURId - 15183