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SMOTE is one of the oversampling techniques for balancing the datasets and itis considered as a pre-processing step in learning algorithms. In this paper,four new enhanced SMOTE are proposed that include an improved version of KNN inwhich the attribute weights are defined by mutual information firstly and thenthey are replaced by maximum entropy, Renyi entropy and Tsallis entropy. Thesefour pre-processing methods are combined with 1NN and J48 classifiers and theirperformance are compared with the previous methods on 11 imbalanced datasetsfrom KEEL repository. The results show that these pre-processing methodsimproves the accuracy compared with the previous stablished works. In addition,as a case study, the first pre-processing method is applied on transportationdata of Tehran-Bazargan Highway in Iran with IR equal to 36.

Modified SMOTE Using Mutual Information and Different Sorts of Entropies