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All Journal International Journal of Electrical and Computer Engineering International Journal of Advances in Applied Sciences TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of electronics, electromedical engineering, and medical informatics Indonesian Journal of Electrical Engineering and Computer Science
Tsehay Admassu Assegie
Aksum University
Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Journal : International Journal of Electrical and Computer Engineering

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Handwritten digits recognition with decision tree classification: a machine learning approach Tsehay Admassu Assegie; Pramod Sekharan Nair
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 5: October 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (482.15 KB) | DOI: 10.11591/ijece.v9i5.pp4446-4451

Abstract

Handwritten digits recognition is an area of machine learning, in which a machine is trained to identify handwritten digits. One method of achieving this is with decision tree classification model. A decision tree classification is a machine learning approach that uses the predefined labels from the past known sets to determine or predict the classes of the future data sets where the class labels are unknown. In this paper we have used the standard kaggle digits dataset for recognition of handwritten digits using a decision tree classification approach. And we have evaluated the accuracy of the model against each digit from 0 to 9.
Co-Authors Haymanot Derebe Bizuneh Pramod Sekharan Nair