Efficient hybrid oversampling and intelligent undersampling for imbalanced big data classification

Carla Vairetti*, José Luis Assadi, Sebastián Maldonado

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Imbalanced classification is a well-known challenge faced by many real-world applications. This issue occurs when the distribution of the target variable is skewed, leading to a prediction bias toward the majority class. With the arrival of the Big Data era, there is a pressing need for efficient solutions to solve this problem. In this work, we present a novel resampling method called SMOTENN that combines intelligent undersampling and oversampling using a MapReduce framework. Both procedures are performed on the same pass over the data, conferring efficiency to the technique. The SMOTENN method is complemented with an efficient implementation of the neighborhoods related to the minority samples. Our experimental results show the virtues of this approach, outperforming alternative resampling techniques for small- and medium-sized datasets while achieving positive results on large datasets with reduced running times.

Original languageEnglish
Article number123149
JournalExpert Systems with Applications
Volume246
DOIs
StatePublished - 15 Jul 2024

Bibliographical note

Publisher Copyright:
© 2024 Elsevier Ltd

Keywords

  • Big data
  • Imbalanced classification
  • Intelligent undersampling
  • MapReduce
  • SMOTE

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