Data Preprocessing for Liver Dataset Using SMOTE

-The class imbalanced problem occurs in various disciplines when one of target classes has a small number of instances compare to other classes. A classifier normally ignores or neglects to detect a minority class due to the small number of class instances. It poses a challenge to any classifier as it becomes hard to learn the minority class samples. Most of the oversampling methods may generate the wrong synthetic minority samples in some scenarios and make learning tasks harder. To overcome this problem in the minority samples first identify the missing attribute data in correctly and learning the task easier. In this paper purpose a new setting of missing data imputation and achieve a high classification rate of Imbalanced liver datasets. To achieve a high classification rate using evolutionary based oversampling, undersampling, Synthetic Minority Over-sampling Technique results are applied to classification using