  SMOTE is one of the oversampling techniques for balancing the datasets and it
is 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 in
which the attribute weights are defined by mutual information firstly and then
they are replaced by maximum entropy, Renyi entropy and Tsallis entropy. These
four pre-processing methods are combined with 1NN and J48 classifiers and their
performance are compared with the previous methods on 11 imbalanced datasets
from KEEL repository. The results show that these pre-processing methods
improves the accuracy compared with the previous stablished works. In addition,
as a case study, the first pre-processing method is applied on transportation
data of Tehran-Bazargan Highway in Iran with IR equal to 36.
