Single-Point Crossover and Jellyfish Optimization for Handling Imbalanced Data Classification Problem

Abeer S. Desuky

doi:10.1109/ACCESS.2022.3146424

Single-Point Crossover and Jellyfish Optimization for Handling Imbalanced Data Classification Problem

2022-01-01

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DOI：10.1109/ACCESS.2022.3146424

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The imbalanced datasets and their classification has pulled in as a hot research topic over the years. It is used in different fields, for example, security, finance, health, and many others. The imbalanced datasets are balanced by applying resampling and various solutions are designed to tackle such datasets that mainly focus on class distribution issues. The imbalanced data is rebalanced using these methods. This paper introduces a technique for balancing data through two stages: first, oversampling methods are utilized in the process of rebalancing such imbalanced dataset using the single-point crossover to generate the new data of minority classes, second, it searches for an optimal subset of the imbalanced and balanced datasets by Jellyfish Search (JS) which is an optimization method. Experiments are performed on 18 real imbalanced datasets, and results are compared with famous oversampling methods and the recently published ACOR (Ant Colony Optimization Resampling) method in terms of different appraisal measurements. Higher performance is recorded by the proposed method and comparability with well-known and recent techniques.

Keywords:

Oceans

Training

Genetics

Support vector machines

Shape

Sea measurements

Sampling methods

Machine learning

imbalanced learning

single-point crossover

data oversampling

jellyfish optimization

Single-Point Crossover and Jellyfish Optimization for Handling Imbalanced Data Classification Problem

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