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

Publication Links

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

IJACSA

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

IJARAI

  • About the Journal
  • Archives
  • Indexing & Archiving

Special Issues

  • Home
  • Archives
  • Proposals
  • Guest Editors

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
  • Guidelines
  • Fees
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Editors
  • Reviewers
  • Subscribe

Article Details

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.

The Effect of Different Dimensionality Reduction Techniques on Machine Learning Overfitting Problem

Author 1: Mustafa Abdul Salam
Author 2: Ahmad Taher Azar
Author 3: Mustafa Samy Elgendy
Author 4: Khaled Mohamed Fouad

Download PDF

Digital Object Identifier (DOI) : 10.14569/IJACSA.2021.0120480

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 12 Issue 4, 2021.

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

Abstract: In most conditions, it is a problematic mission for a machine-learning model with a data record, which has various attributes, to be trained. There is always a proportional relationship between the increase of model features and the arrival to the overfitting of the susceptible model. That observation occurred since not all the characteristics are always important. For example, some features could only cause the data to be noisier. Dimensionality reduction techniques are used to overcome this matter. This paper presents a detailed comparative study of nine dimensionality reduction methods. These methods are missing-values ratio, low variance filter, high-correlation filter, random forest, principal component analysis, linear discriminant analysis, backward feature elimination, forward feature construction, and rough set theory. The effects of used methods on both training and testing performance were compared with two different datasets and applied to three different models. These models are, Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Forest classifier (RFC). The results proved that the RFC model was able to achieve the dimensionality reduction via limiting the overfitting crisis. The introduced RFC model showed a general progress in both accuracy and efficiency against compared approaches. The results revealed that dimensionality reduction could minimize the overfitting process while holding the performance so near to or better than the original one.

Keywords: Dimensionality reduction; feature subset selection; rough set; overfitting; underfitting; machine learning

Mustafa Abdul Salam, Ahmad Taher Azar, Mustafa Samy Elgendy and Khaled Mohamed Fouad, “The Effect of Different Dimensionality Reduction Techniques on Machine Learning Overfitting Problem” International Journal of Advanced Computer Science and Applications(IJACSA), 12(4), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0120480

@article{Salam2021,
title = {The Effect of Different Dimensionality Reduction Techniques on Machine Learning Overfitting Problem},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0120480},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0120480},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {4},
author = {Mustafa Abdul Salam and Ahmad Taher Azar and Mustafa Samy Elgendy and Khaled Mohamed Fouad}
}


IJACSA

Upcoming Conferences

Future of Information and Communication Conference (FICC) 2023

2-3 March 2023

  • Virtual

Computing Conference 2023

22-23 June 2023

  • London, United Kingdom

IntelliSys 2023

7-8 September 2023

  • Amsterdam, The Netherlands

Future Technologies Conference (FTC) 2023

2-3 November 2023

  • San Francisco, United States
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. Registered in England and Wales. Company Number 8933205. All rights reserved. thesai.org