• Register
  • Login

European Journal of Molecular & Clinical Medicine

  • Home
  • Browse
    • Current Issue
    • By Issue
    • By Subject
    • Keyword Index
    • Author Index
    • Indexing Databases XML
  • Journal Info
    • About Journal
    • Aims and Scope
    • Editorial Board
    • Publication Ethics
    • Indexing and Abstracting
    • Peer Review Process
    • News
  • Guide for Authors
  • Submit Manuscript
  • Contact Us
Advanced Search

Notice

As part of Open Journals’ initiatives, we create website for scholarly open access journals. If you are responsible for this journal and would like to know more about how to use the editorial system, please visit our website at https://ejournalplus.com or
send us an email to info@ejournalplus.com

We will contact you soon

  1. Home
  2. Volume 7, Issue 8
  3. Author

Online ISSN: 2515-8260

Volume7, Issue8

An in Depth Analysis of Machine Learning Classifiers for Prediction of Student’s Performance

    Thingbaijam Lenin, N. Chandrasekaran

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 8, Pages 2811-2825

  • Show Article
  • Download
  • Cite
  • Statistics
  • Share

Abstract

Machine learning algorithms are sensitive to the nature and the dimension of the data that are fed into the model for analysis. These algorithms tend to perform significantly different depending upon the dataset used for analysis and training. It then becomes difficult to discover the best algorithm to handle a particular dataset. In the current work, we have made an attempt to verify 24 different state of the art supervised machine learning algorithms in an effort to find the most suitable classifier for predicting the performance of students in our University. Out of the 24 algorithms that we have identified, we found Naïve Bayes (NB) and Stabilized Nearest Neighbor Classifier (SNN) to be the most suitable for deployment followed by K-Nearest Neighbors (KNN) and Cost Sensitive C5.0 (C5.0Cost). We have also determined that handling missing values using KNN improves the classification of minority classes. The classifiers have been evaluated with the sensitivity, specificity, precision, kappa and F-score metrics. It has further been established that the performance metric “Accuracy” is misleading when dealing with imbalanced dataset and balanced accuracy provides far better and reliable information for the model being developed.
Keywords:
  • PDF (471 K)
  • XML
(2020). An in Depth Analysis of Machine Learning Classifiers for Prediction of Student’s Performance. European Journal of Molecular & Clinical Medicine, 7(8), 2811-2825.
Thingbaijam Lenin, N. Chandrasekaran. "An in Depth Analysis of Machine Learning Classifiers for Prediction of Student’s Performance". European Journal of Molecular & Clinical Medicine, 7, 8, 2020, 2811-2825.
(2020). 'An in Depth Analysis of Machine Learning Classifiers for Prediction of Student’s Performance', European Journal of Molecular & Clinical Medicine, 7(8), pp. 2811-2825.
An in Depth Analysis of Machine Learning Classifiers for Prediction of Student’s Performance. European Journal of Molecular & Clinical Medicine, 2020; 7(8): 2811-2825.
  • RIS
  • EndNote
  • BibTeX
  • APA
  • MLA
  • Harvard
  • Vancouver
  • Article View: 126
  • PDF Download: 206
  • LinkedIn
  • Twitter
  • Facebook
  • Google
  • Telegram
Journal Information

Publisher:

Email:  editor.ejmcm21@gmail.com

  • Home
  • Glossary
  • News
  • Aims and Scope
  • Privacy Policy
  • Sitemap

 

For Special Issue Proposal : editor.ejmcm21@gmail.com

This journal is licensed under a Creative Commons Attribution 4.0 International (CC-BY 4.0)

Powered by eJournalPlus