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Knowledge and Information Systems
Published in: Knowledge and Information Systems 3/2024

23-09-2023 | Regular paper

Evidence-based adaptive oversampling algorithm for imbalanced classification

Authors: Chen-ju Lin, Florence Leony

Published in: Knowledge and Information Systems | Issue 3/2024

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Abstract

Classification task is complicated by several facts including skewed class proportion and unclear decision regions due to noise, class overlap, small disjunct, caused by large within-class variation. These issues make data classification difficult, reducing overall performance, and challenging to draw meaningful insights. In this research, the evidence-based adaptive oversampling algorithm (EVA-oversampling) based on Dempster–Shafer theory of evidence is developed for imbalance classification. This technique involves assigning probability regarding class belonging for each instance to represent uncertainty that each data point may hold. Synthetic data points are generated to make up for the under-representation of minority instances on the region with high confidence, thereby strengthening the minority class region. The experiments revealed that the proposed method worked effectively even in situations where imbalanced counts and data complexity would normally pose significant obstacles. This approach performs better than SMOTE, Borderline-SMOTE, ADASYN, MWMOTE, KMeansSMOTE, LoRAS, and SyMProD algorithms in terms of \(F_1\)-measure and G-mean for highly imbalanced data while maintaining the overall performance.
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Metadata
Title
Evidence-based adaptive oversampling algorithm for imbalanced classification
Authors
Chen-ju Lin
Florence Leony
Publication date
23-09-2023
Publisher
Springer London
Published in
Knowledge and Information Systems / Issue 3/2024
Print ISSN: 0219-1377
Electronic ISSN: 0219-3116
DOI
https://doi.org/10.1007/s10115-023-01985-5

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