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Imbalanced Data Classification using Sampling Techniques and XGBoost
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 182 - Number 12 |
| Year of Publication: 2018 |
| Authors: Priyanka Lahoti, Ajeet Kumar Rai |
10.5120/ijca2018917735
|
Priyanka Lahoti, Ajeet Kumar Rai . Imbalanced Data Classification using Sampling Techniques and XGBoost. International Journal of Computer Applications. 182, 12 ( Aug 2018), 19-22. DOI=10.5120/ijca2018917735
Abstract
While implementing any machine learning algorithms it is good to have the descriptive knowledge of the dataset. In any dataset, in case having more than 90% of the data in target variable is from class 1 and the remaining data is from class 2. In such type of dataset, error evaluation metric accuracy is not going to help much. Having the unknown dataset with only class 1 itself gives more than 90% accuracy, which shows accuracy as evaluation metric should be ignored. Such a problem with highly skewed target outcome is known as an Imbalanced classification problem. There is a number of techniques to deal with imbalanced dataset. In this paper, we are interested to see how sampling techniques and XGBoost can be used while working with the Imbalanced dataset.
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