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A self‐adaptive synthetic over‐sampling technique for imbalanced classification

Gu, Xiaowei, Angelov, Plamen P., Soares, Eduardo A. (2020) A self‐adaptive synthetic over‐sampling technique for imbalanced classification. International Journal of Intelligent Systems, 35 (6). pp. 923-943. ISSN 0884-8173. (doi:10.1002/int.22230) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:90182)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
https://doi.org/10.1002/int.22230

Abstract

Traditionally, in supervised machine learning, (a significant) part of the available data (usually 50%-80%) is used for training and the rest—for validation. In many problems, however, the data are highly imbalanced in regard to different classes or does not have good coverage of the feasible data space which, in turn, creates problems in validation and usage phase. In this paper, we propose a technique for synthesizing feasible and likely data to help balance the classes as well as to boost the performance in terms of confusion matrix as well as overall. The idea, in a nutshell, is to synthesize data samples in close vicinity to the actual data samples specifically for the less represented (minority) classes. This has also implications to the so-called fairness of machine learning. In this paper, we propose a specific method for synthesizing data in a way to balance the classes and boost the performance, especially of the minority classes. It is generic and can be applied to different base algorithms, for example, support vector machines, k-nearest neighbour classifiers deep neural, rule-based classifiers, decision trees, and so forth. The results demonstrated that (a) a significantly more balanced (and fair) classification results can be achieved and (b) that the overall performance as well as the performance per class measured by confusion matrix can be boosted. In addition, this approach can be very valuable for the cases when the number of actual available labelled data is small which itself is one of the problems of the contemporary machine learning.

Item Type: Article
DOI/Identification number: 10.1002/int.22230
Uncontrolled keywords: fairness; imbalanced classification; performance boosting; synthetic data generation
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Amy Boaler
Date Deposited: 13 Sep 2021 12:18 UTC
Last Modified: 15 Sep 2021 14:32 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/90182 (The current URI for this page, for reference purposes)
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