Research Article
A Novel Technique on Class Imbalance Big Data using Analogous under Sampling Approach
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 179 - Issue 33 |
| Published: Apr 2018 |
| Authors: Mohammad Imran, Vaddi Srinivasa Rao |
10.5120/ijca2018916743
|
Mohammad Imran, Vaddi Srinivasa Rao . A Novel Technique on Class Imbalance Big Data using Analogous under Sampling Approach. International Journal of Computer Applications. 179, 33 (Apr 2018), 18-21. DOI=10.5120/ijca2018916743
@article{ 10.5120/ijca2018916743,
author = { Mohammad Imran,Vaddi Srinivasa Rao },
title = { A Novel Technique on Class Imbalance Big Data using Analogous under Sampling Approach },
journal = { International Journal of Computer Applications },
year = { 2018 },
volume = { 179 },
number = { 33 },
pages = { 18-21 },
doi = { 10.5120/ijca2018916743 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2018
%A Mohammad Imran
%A Vaddi Srinivasa Rao
%T A Novel Technique on Class Imbalance Big Data using Analogous under Sampling Approach%T
%J International Journal of Computer Applications
%V 179
%N 33
%P 18-21
%R 10.5120/ijca2018916743
%I Foundation of Computer Science (FCS), NY, USA
Abstract
In this paper, we propose hybrid Random under Sampled Imbalance Big Data (USIBD) framework to extract knowledge from class imbalance big data. A novel under-sampling method for the base learner is also proposed to handle the dynamic class-imbalance problem caused by the gradual evolution of classes in big data. The proposed USIBD knowledge discovery framework is robust and less sensitive to outliers where non-uniform distribution of data is applied. Empirical studies demonstrate the effectiveness of USIBD in various class imbalance big datasets scenarios in comparison to existing methods.
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