Journal of Electronics and Informatics
IRO Journals
Volume 2 — Issue 2 — June 2020

Navo Minority Over-sampling Technique (NMOTe): A Consistent Performance Booster on Imbalanced Datasets

https://doi.org/10.36548/jei.2020.2.004
Navoneel Chakrabarty
Jalpaiguri Government Engineering College, Jalpaiguri
Sanket Biswas
Autonomous University of Barcelona, Spain
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How to Cite

Chakrabarty, Navoneel, and Sanket Biswas. 2020. “Navo Minority Over-Sampling Technique (NMOTe): A Consistent Performance Booster on Imbalanced Datasets”. Journal of Electronics and Informatics 2 (2): 96-136. https://doi.org/10.36548/jei.2020.2.004.
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Keywords

— imbalanced data
— machine learning
— classification
— data mining
— over-sampling
— under-sampling
— SMOTe
— NMOTe
Published: 06-06-2020

Abstract

Imbalanced data refers to a problem in machine learning where there exists unequal distribution of instances for each classes. Performing a classification task on such data can often turn bias in favour of the majority class. The bias gets multiplied in cases of high dimensional data. To settle this problem, there exists many real-world data mining techniques like over-sampling and under-sampling, which can reduce the Data Imbalance. Synthetic Minority Oversampling Technique (SMOTe) provided one such state-of-the-art and popular solution to tackle class imbalancing, even on high-dimensional data platform. In this work, a novel and consistent oversampling algorithm has been proposed that can further enhance the performance of classification, especially on binary imbalanced datasets. It has been named as NMOTe (Navo Minority Oversampling Technique), an upgraded and superior alternative to the existing techniques. A critical analysis and comprehensive overview on the literature has been done to get a deeper insight into the problem statements and nurturing the need to obtain the most optimal solution. The performance of NMOTe on some standard datasets has been established in this work to get a statistical understanding on why it has edged the existing state-of-the-art to become the most robust technique for solving the two-class data imbalance problem.

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