Feature dimensionality reduction for recognition of Persian handwritten letters using a combination of quantum genetic algorithm and neural network

  • Mohammad Javad Aranian Imam Reza International University
  • Moein Sarvaghad-Moghaddam Semnan University http://orcid.org/0000-0003-1878-546X
  • Monireh Houshmand Imam Reza International University
Keywords: dimensionality reduction of features, recognition of Persian handwritten letters, genetic algorithm (GA), quantum genetic algorithm (QGA), neural networks

Abstract

Curse of dimensionality is one of the biggest challenges in classification problems. High dimensionality of problem increases classification rate and brings about classification error. Selecting an effective subset of features is an important point in analyzing correlation rate in classification issues. The main purpose of this paper is enhancing characters recognition and classification, creating quick and low-cost classes, and eventually recognizing Persian handwritten characters more accurately and faster. In this paper, to reduce feature dimensionality of datasets a hybrid approach using artificial neural network, genetic algorithm and quantum genetic algorithm is proposed that can be used to distinguish Persian handwritten letters. Implementation results show that proposed algorithms are able to reduce number of features by 19% to 49%. They also show that recognition and classification accuracy of resulted subset of features has risen, by 7/31%, comparing to primitive dataset.

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Published
2017-06-01
How to Cite
Aranian, M. J., Sarvaghad-Moghaddam, M., & Houshmand, M. (2017). Feature dimensionality reduction for recognition of Persian handwritten letters using a combination of quantum genetic algorithm and neural network. Majlesi Journal of Electrical Engineering, 11(2). Retrieved from http://mjee.iaumajlesi.ac.ir/index/index.php/ee/article/view/2181
Section
Articles