Affiliations: Department of Computer Science and Applications, SCSVMV University, Enathur, Kancheepuram, Tamilnadu, India
Correspondence:
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Corresponding author. S. Babu, Assistant Professor, Department of Computer Science and Applications, SCSVMV University, Enathur, Kanchipuram 631 561, Tamilnadu, India. E-mail: [email protected].
Abstract: Research focus increases rapidly on recent years in mining imbalanced data set, because of its challenge and its extensive application on the real world. A dataset is said to be imbalance, if categories of the classification attribute is not evenly represented. A fine balanced dataset is an important source for the classifiers to define the best prediction model. All the existing classifiers are inclined to perform poor on the imbalanced datasets. The reason for this is, all the classifiers seek to optimize their overall accuracy not by considering the relative distribution of each class. Hence, it is very essential to go for well balanced dataset for classification. In this paper, the comprehensive Enhanced Minority Oversampling TEchnique (EMOTE) is proposed to improve the performance of the classifier by balancing the dataset. The key idea of the proposed method is to balance the dataset by tuning the misclassified instances of the minority classes into correctly classified instances through oversampling their nearest neighbor. To investigate the performance of the proposed model, different oversampling and under sampling methods inclusive of the well known method SMOTE (Synthetic Minority Oversampling TEchnique) are considered. Various imbalanced datasets from the UCI machine learning repository are considered for experiments The experimental results shows that, the proposed method EMOTE outperformed the other methods in balancing the dataset. In addition to this it is also proved that, the classifier is able to effectively improve its performance on the dataset which is generated by EMOTE.
Keywords: Imbalanced dataset, classification, nearest neighbor, oversampling, under sampling
