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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 - Number 33 |
| Year of Publication: 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
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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