ew 20(27): e2

Research Article

Over-sampling imbalanced datasets using the Covariance Matrix

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  • @ARTICLE{10.4108/eai.13-7-2018.163982,
        author={Ireimis Leguen-deVarona and Julio Madera and Yoan Mart\^{\i}nez-L\^{o}pez and Jos\^{e} Carlos Hern\^{a}ndez-Nieto},
        title={Over-sampling imbalanced datasets using the Covariance Matrix},
        journal={EAI Endorsed Transactions on Energy Web},
        keywords={Imbalanced datasets, Oversampling, Covariance Matrix, Attribute Dependency},
  • Ireimis Leguen-deVarona
    Julio Madera
    Yoan Martínez-López
    José Carlos Hernández-Nieto
    Year: 2020
    Over-sampling imbalanced datasets using the Covariance Matrix
    DOI: 10.4108/eai.13-7-2018.163982
Ireimis Leguen-deVarona1, Julio Madera1,*, Yoan Martínez-López1, José Carlos Hernández-Nieto1
  • 1: University of Camagüey, Camagüey, Cuba
*Contact email: julio.madera@reduc.edu.cu


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” this problem 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 of KNN to balance datasets, with continuous attributes and binary class.

METHODS: We implemented two variants SMOTE-CovI, which generates new values within the interval of each 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 minority class, using the Covariance Matrix.