Classification algorithms for determining handwritten digit
المؤلف
al-Bahadili, Haydar Nasir Khuraybit
المصدر
The Iraqi Journal of Electrical and Electronic Engineering
العدد
المجلد 12، العدد 1 (30 يونيو/حزيران 2016)، ص ص. 96-102، 7ص.
الناشر
تاريخ النشر
2016-06-30
دولة النشر
العراق
عدد الصفحات
7
التخصصات الرئيسية
الملخص 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.
نمط استشهاد جمعية علماء النفس الأمريكية (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
نمط استشهاد الجمعية الأمريكية للغات الحديثة (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
نمط استشهاد الجمعية الطبية الأمريكية (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
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references : p. 101-102
رقم السجل
BIM-691596
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر