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Real world datasets are heavily skewed where some classes are significantlyoutnumbered by the other classes. In these situations, machine learningalgorithms fail to achieve substantial efficacy while predicting theseunder-represented instances. To solve this problem, many variations ofsynthetic minority over-sampling methods (SMOTE) have been proposed to balancethe dataset which deals with continuous features. However, for datasets withboth nominal and continuous features, SMOTE-NC is the only SMOTE-basedover-sampling technique to balance the data. In this paper, we present a novelminority over-sampling method, SMOTE-ENC (SMOTE - Encoded Nominal andContinuous), in which, nominal features are encoded as numeric values and thedifference between two such numeric value reflects the amount of change ofassociation with minority class. Our experiments show that the classificationmodel using SMOTE-ENC method offers better prediction than model using SMOTE-NCwhen the dataset has a substantial number of nominal features and also whenthere is some association between the categorical features and the targetclass. Additionally, our proposed method addressed one of the major limitationsof SMOTE-NC algorithm. SMOTE-NC can be applied only on mixed datasets that havefeatures consisting of both continuous and nominal features and cannot functionif all the features of the dataset are nominal. Our novel method has beengeneralized to be applied on both mixed datasets and on nominal only datasets.The code is available from mkhushi.github.io

SMOTE-ENC: A novel SMOTE-based method to generate synthetic data for nominal and continuous features