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Classification algorithms for determining handwritten digit
Author
al-Bahadili, Haydar Nasir Khuraybit
Source
The Iraqi Journal of Electrical and Electronic Engineering
Issue
Vol. 12, Issue 1 (30 Jun. 2016), pp.96-102, 7 p.
Publisher
University of Basrah College of Engineering
Publication Date
2016-06-30
Country of Publication
Iraq
No. of Pages
7
Main Subjects
Abstract EN
Data-intensive science is a critical science paradigm that interferes with all other sciences.
Data mining (DM) is a powerful and useful technology with wide potential users focusing on important meaningful patterns and discovers a new knowledge from a collected dataset.
Any predictive task in DM uses some attribute to classify an unknown class.
Classification algorithms are a class of prominent mathematical techniques in DM.
Constructing a model is the core aspect of such algorithms.
However, their performance highly depends on the algorithm behavior upon manipulating data.
Focusing on binarazaition as an approach for preprocessing, this paper analysis and evaluates different classification algorithms when construct a model based on accuracy in the classification task.
The Mixed National Institute of Standards and Technology (MNIST) handwritten digits dataset provided by Yann LeCun has been used in evaluation.
The paper focuses on machine learning approaches for handwritten digits detection.
Machine learning establishes classification methods, such as K-Nearest Neighbor(KNN), Decision Tree (DT), and Neural Networks (NN).
Results showed that the knowledge-based method, i.e.
NN algorithm, is more accurate in determining the digits as it reduces the error rate.
The implication of this evaluation is providing essential insights for computer scientists and practitioners for choosing the suitable DM technique that fit with their data.
American Psychological Association (APA)
al-Bahadili, Haydar Nasir Khuraybit. 2016. Classification algorithms for determining handwritten digit. The Iraqi Journal of Electrical and Electronic Engineering،Vol. 12, no. 1, pp.96-102.
https://search.emarefa.net/detail/BIM-691596
Modern Language Association (MLA)
al-Bahadili, Haydar Nasir Khuraybit. Classification algorithms for determining handwritten digit. The Iraqi Journal of Electrical and Electronic Engineering Vol. 12, no. 1 (2016), pp.96-102.
https://search.emarefa.net/detail/BIM-691596
American Medical Association (AMA)
al-Bahadili, Haydar Nasir Khuraybit. Classification algorithms for determining handwritten digit. The Iraqi Journal of Electrical and Electronic Engineering. 2016. Vol. 12, no. 1, pp.96-102.
https://search.emarefa.net/detail/BIM-691596
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 101-102
Record ID
BIM-691596