Handwritten Digits Recognition Using an EnsembleTechnique Based on the Firefly Algorithm

Publish Year: 1397
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:

JR_JIST-6-3_003

تاریخ نمایه سازی: 6 اسفند 1398

Abstract:

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.

Authors

Hamed Agahi

Department of Electrical Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran

Azar Mahmoodzadeh

Department of Electrical Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran

Marzieh Salehi

Department of Electrical Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran