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

Majlesi Journal of Electrical Engineering

مقاله 3، دوره 11، شماره 2، شهریور 2017   

نوع مقاله: Review Article

نویسندگان

Mohammad Javad Aranian¹؛ Moein Sarvaghad-Moghaddam^{* 2}؛ Monireh Houshmand¹

¹Imam Reza International University

²Semnan University

چکیده

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.

کلیدواژه‌ها

dimensionality reduction of features؛ en؛ recognition of Persian handwritten letters؛ Genetic Algorithm (GA)؛ quantum genetic algorithm (QGA)؛ Neural Networks

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