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Open AccessArticle

Data Sampling Methods to Deal With the Big Data Multi-Class Imbalance Problem

1
Division of Postgraduate Studies and Research, National Institute of Technology of Mexico, IT Toluca, Av. Tecnológico s/n, Agrícola Bellavista, 52149 Metepec, Mexico
2
UAEM University Center at Atlacomulco, Autonomous University of the State of Mexico, Carretera Toluca-Atlacomulco Km. 60, 50450 Atlacomulco, Mexico
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Appl. Sci. 2020, 10(4), 1276; https://doi.org/10.3390/app10041276
Received: 2 January 2020 / Revised: 3 February 2020 / Accepted: 10 February 2020 / Published: 14 February 2020
The class imbalance problem has been a hot topic in the machine learning community in recent years. Nowadays, in the time of big data and deep learning, this problem remains in force. Much work has been performed to deal to the class imbalance problem, the random sampling methods (over and under sampling) being the most widely employed approaches. Moreover, sophisticated sampling methods have been developed, including the Synthetic Minority Over-sampling Technique (SMOTE), and also they have been combined with cleaning techniques such as Editing Nearest Neighbor or Tomek’s Links (SMOTE+ENN and SMOTE+TL, respectively). In the big data context, it is noticeable that the class imbalance problem has been addressed by adaptation of traditional techniques, relatively ignoring intelligent approaches. Thus, the capabilities and possibilities of heuristic sampling methods on deep learning neural networks in big data domain are analyzed in this work, and the cleaning strategies are particularly analyzed. This study is developed on big data, multi-class imbalanced datasets obtained from hyper-spectral remote sensing images. The effectiveness of a hybrid approach on these datasets is analyzed, in which the dataset is cleaned by SMOTE followed by the training of an Artificial Neural Network (ANN) with those data, while the neural network output noise is processed with ENN to eliminate output noise; after that, the ANN is trained again with the resultant dataset. Obtained results suggest that best classification outcome is achieved when the cleaning strategies are applied on an ANN output instead of input feature space only. Consequently, the need to consider the classifier’s nature when the classical class imbalance approaches are adapted in deep learning and big data scenarios is clear. View Full-Text
Keywords: big data; multi-class imbalance problem; sampling methods; hyper-spectral remote sensing images big data; multi-class imbalance problem; sampling methods; hyper-spectral remote sensing images
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Rendón, E.; Alejo, R.; Castorena, C.; Isidro-Ortega, F.J.; Granda-Gutiérrez, E.E. Data Sampling Methods to Deal With the Big Data Multi-Class Imbalance Problem. Appl. Sci. 2020, 10, 1276.

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