Over-sampling imbalanced datasets using the Covariance Matrix

Abstract

INTRODUCTION: Nowadays, many machine learning tasks involve learning from imbalanced datasets,leading to the miss-classification of the minority class. One of the state-of-the-art approaches to ”solve” thisproblem at the data level is Synthetic Minority Over-sampling Technique (SMOTE) which in turn uses KNearest Neighbors (KNN) algorithm to select and generate new instances.OBJECTIVES: This paper presents SMOTE-Cov, a modified SMOTE that use Covariance Matrix instead ofKNN to balance datasets, with continuous attributes and binary class.METHODS: We implemented two variants SMOTE-CovI, which generates new values within the interval ofeach attribute and SMOTE-CovO, which allows some values to be outside the interval of the attributes.RESULTS: The results show that our approach has a similar performance as the state- of-the-art approaches.CONCLUSION: In this paper, a new algorithm is proposed to generate synthetic instances of the minorityclass, using the Covariance Matrix.

Keywords

• imbalanced datasets
• oversampling
• covariance matrix
• attribute dependency