A Review on Data Level Approaches for Managing Imbalanced Classification Problem

Authors

  • Ayushi Chaplot  Department of CSE, College of Technology and Engineering, Udaipur, Rajasthan, India
  • Naveen Choudhary  Department of CSE, College of Technology and Engineering, Udaipur, Rajasthan, India
  • Kalpana Jain  Department of CSE, College of Technology and Engineering, Udaipur, Rajasthan, India

DOI:

https://doi.org/10.32628/IJSRSET196225

Keywords:

Imbalanced data, Oversampling, Undersampling, Multiclass Classification.

Abstract

In real world, the distribution of dataset is not in symmetric form. It can vary from application to application and distribution of data in that application. The un-symmetric form of this distribution is called imbalanced class distribution or skewed class distribution. So, the classification of data with skewed distribution of class can lead to the poor performance of the classifier. To solve the problem of imbalanced dataset in which the instances of one class is more than the instances of other class, there are different data level approaches for handling imbalanced classes. So, in this paper we will discuss about different data level approaches and have comparative study among them.

References

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International Journal of Scientific Research in Science, Engineering and Technology

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Published

2019-04-30

Issue

Volume 6, Issue 2, March-April-2019

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Ayushi Chaplot, Naveen Choudhary, Kalpana Jain "A Review on Data Level Approaches for Managing Imbalanced Classification Problem" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 6, Issue 2, pp.91-97, March-April-2019. Available at doi : https://doi.org/10.32628/IJSRSET196225