Open AccessHandwritten digits recognition with decision tree classification: a machine learning approach
Author(s) -
Tsehay Admassu Assegie,
Pramod S. Nair
Publication year - 2019
Publication title -
international journal of power electronics and drive systems/international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
eISSN - 2722-2578
pISSN - 2722-256X
DOI - 10.11591/ijece.v9i5.pp4446-4451
Subject(s) - decision tree , computer science , artificial intelligence , pattern recognition (psychology) , digit recognition , numerical digit , machine learning , decision tree learning , tree (set theory) , artificial neural network , mathematics , arithmetic , mathematical analysis
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.