Combining Synthetic Minority Oversampling Technique and Subset Feature Selection Technique For Class Imbalance Problem
Pawan Lachheta, Seema Bawa · 2016
Building an effective classification model when the high dimensional data is suffering from class imbalance problem is a major challenge. The problem is severe when negative samples have large percentages than positive samples. To surmount the class imbalance and high dimensionality issues in the dataset, we propose a SFS framework that comprises of SMOTE filters, which are used for balancing the datasets, as well as feature ranker for pre-processing of data. The framework is developed using R language and various R packages. Then the performance of SFS framework is evaluated and found that proposed framework outperforms than other state-of-the-art methods.