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FG-SMOTE: Fuzzy-based Gaussian synthetic minority oversampling with deep belief networks classifier for skewed class distribution

Putta Hemalatha (SCOPE, Vellore Institute of Technology, Vellore, India)
Geetha Mary Amalanathan (SCOPE, Vellore Institute of Technology, Vellore, India)

International Journal of Intelligent Computing and Cybernetics

ISSN: 1756-378X

Article publication date: 15 March 2021

Issue publication date: 23 April 2021

104

Abstract

Purpose

Adequate resources for learning and training the data are an important constraint to develop an efficient classifier with outstanding performance. The data usually follows a biased distribution of classes that reflects an unequal distribution of classes within a dataset. This issue is known as the imbalance problem, which is one of the most common issues occurring in real-time applications. Learning of imbalanced datasets is a ubiquitous challenge in the field of data mining. Imbalanced data degrades the performance of the classifier by producing inaccurate results.

Design/methodology/approach

In the proposed work, a novel fuzzy-based Gaussian synthetic minority oversampling (FG-SMOTE) algorithm is proposed to process the imbalanced data. The mechanism of the Gaussian SMOTE technique is based on finding the nearest neighbour concept to balance the ratio between minority and majority class datasets. The ratio of the datasets belonging to the minority and majority class is balanced using a fuzzy-based Levenshtein distance measure technique.

Findings

The performance and the accuracy of the proposed algorithm is evaluated using the deep belief networks classifier and the results showed the efficiency of the fuzzy-based Gaussian SMOTE technique achieved an AUC: 93.7%. F1 Score Prediction: 94.2%, Geometric Mean Score: 93.6% predicted from confusion matrix.

Research limitations/implications

The proposed research still retains some of the challenges that need to be focused such as application FG-SMOTE to multiclass imbalanced dataset and to evaluate dataset imbalance problem in a distributed environment.

Originality/value

The proposed algorithm fundamentally solves the data imbalance issues and challenges involved in handling the imbalanced data. FG-SMOTE has aided in balancing minority and majority class datasets.

Keywords

Acknowledgements

Disclosure Statement: No potential conflict of interest was reported by the authors.

Citation

Hemalatha, P. and Amalanathan, G.M. (2021), "FG-SMOTE: Fuzzy-based Gaussian synthetic minority oversampling with deep belief networks classifier for skewed class distribution", International Journal of Intelligent Computing and Cybernetics, Vol. 14 No. 2, pp. 270-287. https://doi.org/10.1108/IJICC-12-2020-0202

Publisher

:

Emerald Publishing Limited

Copyright © 2021, Emerald Publishing Limited

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