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Mexican Conference on Pattern Recognition

MCPR 2022: Pattern Recognition pp 46–55Cite as

A Preliminary Study of SMOTE on Imbalanced Big Datasets When Dealing with Sparse and Dense High Dimensionality

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Part of the Lecture Notes in Computer Science book series (LNCS,volume 13264)

Abstract

The interest in exploiting big datasets with machine learning has led to adapting classic strategies in this new paradigm determined by volume, speed, and variety. Because data quality is a determining factor in constructing a classifier, it has also been necessary to adapt or develop new data preprocessing techniques. One of the challenges of most significant interest is the class imbalance problem, where the class of interest has a smaller number of examples concerning another class called the majority. To alleviate this problem, one of the most recognized techniques is SMOTE, which is characterized by generating instances of the minority class through a process that uses the nearest neighbor rule and the Euclidean distance. Various articles have shown that SMOTE is not appropriate for datasets with high dimensionality. However, in big data, datasets with high dimensionality have contained many zeros. Therefore, in this article, our objective is to analyze the SMOTE-BD behavior on imbalanced big datasets with sparse and dense dimensionality. Experimental results using two classifiers and big datasets with different dimensionalities suggest that sparsity is a predominant factor than the dimensionality in the behavior of SMOTE-BD.

Keywords

  • Big data
  • SMOTE
  • Class imbalance
  • High dimensionality
  • Dense dataset
  • Sparse dataset

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Fig. 1.
Fig. 2.

Notes

  1. 1.

    Although this medium-high dimensional dataset may not represent a big data problem in terms of volume, we believe it can be treated as such since it may not be processed and analyzed on standard hardware.

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Authors and Affiliations

  1. Doctorado en Ciencias de la Ingenería Avanzada,Institituto de Ingeniería y Tecnología, Universidad Autónoma de Ciudad Juárez, Chihuahua, Mexico

    A. Bolívar

  2. División Multidisciplinaria en Ciudad Universitaria, Universidad Autónoma de Ciudad Juárez, Chihuahua, Mexico

    V. García, R. Florencia, G. Rivera & J. Patricia Sánchez-Solís

  3. Tecnológico Nacional de México, IT Toluca, Metepec, Mexico

    R. Alejo

Authors
  1. A. Bolívar
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  2. V. García
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  3. R. Florencia
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  4. R. Alejo
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  5. G. Rivera
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  6. J. Patricia Sánchez-Solís
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Corresponding author

Correspondence to A. Bolívar .

Editor information

Editors and Affiliations

  1. Universidad Autónoma de Ciudad Juárez, Ciudad Juárez, Mexico

    Prof. Dr. Osslan Osiris Vergara-Villegas

  2. Universidad Autónoma de Ciudad Juárez, Ciudad Juárez, Mexico

    Vianey Guadalupe Cruz-Sánchez

  3. Instituto Politécnico Nacional, Mexico City, Mexico

    Juan Humberto Sossa-Azuela

  4. Instituto Nacional de Astrofísica, Óptica y Electrónica, Puebla, Mexico

    Dr. Jesús Ariel Carrasco-Ochoa

  5. Instituto Nacional de Astrofísica, Óptica y Electrónica, Puebla, Mexico

    José Francisco Martínez-Trinidad

  6. Autonomous University of Puebla, Puebla, Mexico

    José Arturo Olvera-López

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Bolívar, A., García, V., Florencia, R., Alejo, R., Rivera, G., Sánchez-Solís, J.P. (2022). A Preliminary Study of SMOTE on Imbalanced Big Datasets When Dealing with Sparse and Dense High Dimensionality. In: Vergara-Villegas, O.O., Cruz-Sánchez, V.G., Sossa-Azuela, J.H., Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F., Olvera-López, J.A. (eds) Pattern Recognition. MCPR 2022. Lecture Notes in Computer Science, vol 13264. Springer, Cham. https://doi.org/10.1007/978-3-031-07750-0_5

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  • DOI: https://doi.org/10.1007/978-3-031-07750-0_5

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