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Handwritten digits recognition with decision tree classification: a machine learning approach

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 9 No. 5 (2019)

DOI
10.11591/ijece.v9i5.pp4446-4451
ISSN
2722-2578, 2088-8708
Published
2019-10-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-sa/4.0

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.

Collaboration network of the authors

20 authors · 61 co-authorship links · 42 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Aksum University
  • St. Peter's Institute of Higher Education and Research
  • Injibara University
  • Koneru Lakshmaiah Education Foundation
  • Afe Babalola University
  • Rivers State University
  • University of Port Harcourt
  • Other organisations
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Ayodeji Olalekan SalauAfe Babalola University4
Tamilarasi SureshSt. Peter's Institute of Higher Education and Research3
Napa Komal KumarSt. Peter's Institute of Higher Education and Research2
Tsrity Asefa BerihuAksum University1
Varun KumarAksum University1
R. SubhashniSt. Peter's Institute of Higher Education and Research1
Crescent Onyebuchi OmejeRivers State University1
Sepiribo Lucky BraideUniversity of Port Harcourt1
Ravulapalli Lakshmi TulasiR.V. College of Engineering1
Sangeetha GanesanAffiliation unknown1
Radha MothukuriKoneru Lakshmaiah Education Foundation1
Elisha Didam MarkusCentral University of Technology1
Joy Nnenna EnehUniversity of Nigeria1
Thankgod Izuchukwu OzueSaveetha University1
Amogne Andualem AyalewMizan Tepi University1
Melaku Lake TegegneInjibara University1
Bommy ManivannanAffiliation unknown1
Battula Krishna PrasadKoneru Lakshmaiah Education Foundation1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>Handwritten digits recognition with decision tree classification: a machine learning approach</dc:title><dc:creator>Assegie, Tsehay Admassu</dc:creator><dc:creator>Nair, Pramod Sekharan</dc:creator><dc:description>&lt;jats:p&gt;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.&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2019-10-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v9i5.pp4446-4451</dc:identifier><dc:identifier>10.11591/ijece.v9i5.pp4446-4451</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 9 No 5 (2019); 4446</dc:source><dc:source>2722-2578</dc:source><dc:source>2088-8708</dc:source><dc:relation>http://ijece.iaescore.com/index.php/IJECE/article/viewFile/18004/13056</dc:relation><dc:rights>http://creativecommons.org/licenses/by-sa/4.0</dc:rights></oai_dc:dc>