The Science and Information (SAI) Organization
  • Home
  • About Us
  • Journals
  • Conferences
  • Contact Us

Publication Links

  • IJACSA
  • Author Guidelines
  • Publication Policies
  • Digital Archiving Policy
  • Promote your Publication
  • Metadata Harvesting (OAI2)

IJACSA

  • About the Journal
  • Call for Papers
  • Editorial Board
  • Author Guidelines
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Fees/ APC
  • Reviewers
  • Apply as a Reviewer

IJARAI

  • About the Journal
  • Archives
  • Indexing & Archiving

Special Issues

  • Home
  • Archives
  • Proposals
  • Guest Editors
  • SUSAI-EE 2025
  • ICONS-BA 2025
  • IoT-BLOCK 2025

Future of Information and Communication Conference (FICC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Computing Conference

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Intelligent Systems Conference (IntelliSys)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Future Technologies Conference (FTC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact
  • Home
  • Call for Papers
  • Editorial Board
  • Guidelines
  • Submit
  • Current Issue
  • Archives
  • Indexing
  • Fees
  • Reviewers
  • Subscribe

DOI: 10.14569/IJACSA.2017.081201
PDF

Impact of Different Data Types on Classifier Performance of Random Forest, Naïve Bayes, and K-Nearest Neighbors Algorithms

Author 1: Asmita Singh
Author 2: Malka N. Halgamuge
Author 3: Rajasekaran Lakshmiganthan

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 8 Issue 12, 2017.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: This study aims to evaluate impact of three different data types (Text only, Numeric Only and Text + Numeric) on classifier performance (Random Forest, k-Nearest Neighbor (kNN) and Naïve Bayes (NB) algorithms). The classification problems in this study are explored in terms of mean accuracy and the effects of varying algorithm parameters over different types of datasets. This content analysis has been examined through eight different datasets taken from UCI to train models for all three algorithms. The results obtained from this study clearly show that RF and kNN outperform NB. Furthermore, kNN and RF perform relatively the same in terms of mean accuracy nonetheless kNN takes less time to train a model. The changing numbers of attributes in datasets have no effect on Random Forest, whereas Naïve Bayes mean accuracy fluctuates up and down that leads to a lower mean accuracy, whereas, kNN mean accuracy increases and ends with higher accuracy. Additionally, changing number of trees has no significant effects on mean accuracy of the Random forest, however, the time to train the model has increased greatly. Random Forest and k-Nearest Neighbor are proved to be the best classifiers for any type of dataset. Thus, Naïve Bayes can outperform other two algorithms if the feature variables are in a problem space and are independent. Besides Random forests, it takes highest computational time and Naïve Bayes takes lowest. The k-Nearest Neighbor requires finding an optimal number of k for improved performance at the cost of computation time. Similarly, changing the number of attributes that effect Naïve Bayes and k-Nearest Neighbor performance nevertheless not the Random forest. This study can be extended by researchers who use the parametric method to analyze results.

Keywords: Big data; random forest; Naïve Bayes; k-nearest neighbors algorithm

Asmita Singh, Malka N. Halgamuge and Rajasekaran Lakshmiganthan, “Impact of Different Data Types on Classifier Performance of Random Forest, Naïve Bayes, and K-Nearest Neighbors Algorithms ” International Journal of Advanced Computer Science and Applications(IJACSA), 8(12), 2017. http://dx.doi.org/10.14569/IJACSA.2017.081201

@article{Singh2017,
title = {Impact of Different Data Types on Classifier Performance of Random Forest, Naïve Bayes, and K-Nearest Neighbors Algorithms },
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2017.081201},
url = {http://dx.doi.org/10.14569/IJACSA.2017.081201},
year = {2017},
publisher = {The Science and Information Organization},
volume = {8},
number = {12},
author = {Asmita Singh and Malka N. Halgamuge and Rajasekaran Lakshmiganthan}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

IJACSA

Upcoming Conferences

IntelliSys 2025

28-29 August 2025

  • Amsterdam, The Netherlands

Future Technologies Conference 2025

6-7 November 2025

  • Munich, Germany

Healthcare Conference 2026

21-22 May 2026

  • Amsterdam, The Netherlands

Computing Conference 2026

9-10 July 2026

  • London, United Kingdom

IntelliSys 2026

3-4 September 2026

  • Amsterdam, The Netherlands

Computer Vision Conference 2026

15-16 October 2026

  • Berlin, Germany
The Science and Information (SAI) Organization
BACK TO TOP

Computer Science Journal

  • About the Journal
  • Call for Papers
  • Submit Paper
  • Indexing

Our Conferences

  • Computing Conference
  • Intelligent Systems Conference
  • Future Technologies Conference
  • Communication Conference

Help & Support

  • Contact Us
  • About Us
  • Terms and Conditions
  • Privacy Policy

© The Science and Information (SAI) Organization Limited. All rights reserved. Registered in England and Wales. Company Number 8933205. thesai.org