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An Approach to Improve the Detection rate using Sampling of Imbalanced Data

Synthetic Minority Over-sampling Technique (SMOTE) works by creating synthetic observations based upon the existing minority observations. In this research KDD Cup99 dataset is used. Through SMOTE we tried to increase the rare classes (U2R and R2L).The random forest was used to create the model in the Cost Sensitive Classifier. The tests were performed on many percentage ratios of rare classes. Results were better than the existing one.