Abstract

Imbalance in dataset enforces numerous challenges to implement data analytic in all existing real world applications using machine learning. Data imbalance occurs when sample size from a class is very small or large then another class. Performance of predicted models is greatly affected when dataset is highly imbalanced and sample size increases. Overall, Imbalanced training data have a major negative impact on performance. Leading machine learning technique combat with imbalanced dataset by focusing on avoiding the minority class and reducing the inaccuracy for the majority class. This article presents a review of different approaches to classify imbalanced dataset and their application areas.

Details

Title
Classification of Imbalanced Data:Review of Methods and Applications
Author
Kumar, Pradeep 1 ; Bhatnagar, Roheet 2 ; Gaur, Kuntal 1 ; Bhatnagar, Anurag 3 

 Department of Computer Applications,Manipal University Jaipur, Jaipur-Ajmer Express Highway, Dehmi Kalan, Near GVK Toll Plaza, Jaipur, Rajasthan-303007 
 Department of Computer Science and Engineering,Manipal University Jaipur, Jaipur-Ajmer Express Highway, Dehmi Kalan, Near GVK Toll Plaza, Jaipur, Rajasthan-303007 
 Department of Information Technology,Manipal University Jaipur, Jaipur-Ajmer Express Highway, Dehmi Kalan, Near GVK Toll Plaza, Jaipur, Rajasthan-303007 
Publication year
2021
Publication date
Mar 2021
Publisher
IOP Publishing
ISSN
17578981
e-ISSN
1757899X
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2512951886
Copyright
© 2021. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.