Research Article
Over-sampling imbalanced datasets using the Covariance Matrix
@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}, volume={7}, number={27}, publisher={EAI}, journal_a={EW}, year={2020}, month={4}, keywords={Imbalanced datasets, Oversampling, Covariance Matrix, Attribute Dependency}, doi={10.4108/eai.13-7-2018.163982} }- Ireimis Leguen-deVarona
Julio Madera
Yoan Martínez-López
José Carlos Hernández-Nieto
Year: 2020
Over-sampling imbalanced datasets using the Covariance Matrix
EW
EAI
DOI: 10.4108/eai.13-7-2018.163982
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” 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.
Copyright © 2020 I. Leguen-deVaronaet al., licensed to EAI. This is an open access article distributed under the terms ofthe Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/), which permits unlimiteduse, distribution and reproduction in any medium so long as the original work is properly cited.