International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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Volume 179 - Issue 33 |
Published: Apr 2018 |
Authors: Mohammad Imran, Vaddi Srinivasa Rao |
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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
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.