International Journal of Advanced
Science and Engineering Research
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ENHANCING THE PERFORMANCE OF THE CLASSIFIER IN THE CONTEXT OF IMBALACED DATASET USING EMOTE+UNDERSAMPLING
Abstract
Data Mining is used to analyze the data, to generate useful knowledge. In Data Mining, Classifiers are the technique for prediction. Balanced dataset is a vital source for the classifiers to produce the best prediction. Sampling are the techniques to handle such an issue. In this paper an Enhanced hybrid model was proposed to balance the dataset, which is the integration of both undersampling and oversampling techniques. In order to balance the dataset, the model initially uses undersampling technique to remove certain instance from majority class which has less classification information and then oversampling technique applied on minority class by using nearest neighbors. To prove the efficiency of the proposed model various experiments were conducted. To perform the same datasets with different imbalance ratio were taken from repository. The results of the experiments show that classifiers were able to perform well on the dataset which was balanced by the proposed model.