A Multiple Resampling Method for Learning from Imbalanced Data Sets

A Multiple Resampling Method for Learning from Imbalanced Data Sets
Author(s):
A.KANIMOZHI
Keywords:

Abstract
Re-Sampling methods are commonly used for dealing with the class-imbalance problem. Their advantage over other methods is that they are external and thus, easily transportable. Although such approaches can be very simple to implement, tuning them most effectively is not an easy task. In particular, it is unclear whether oversampling is more effective than undersampling and which oversampling or undersampling rate should be used. This paper presents an experimental study of these questions and concludes that combining different expressions of the re-sampling approach is an effective solution to the tuning problem. The proposed combination scheme is evaluated on imbalanced subsets of the Reuters-21578 text collection and is shown to be quite effective for these problems
Article Details
Unique Paper ID: 145459 Publication Volume & Issue: Volume 4, Issue 10 Page(s): 943 - 952
Article Preview & Download

Share This Article

NCSEM 2024

National Conference on Sustainable Engineering and Management - 2024

Last Date: 15th March 2024

IJIRT.org enables door in research by providing high quality research articles in open access market.

Send us any query related to your research on editor@ijirt.org