Abstract
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.
Highlights
M ACHINE learning (ML) techniques play a vital role in gaining insights from the data in different repositories that are growing exponentially
Three approaches are suggested for such techniques (1) Data level methods focus on sampling the instances of majority and minority classes for balancing the distribution; (2) Algorithm-level techniques focus on adapting current learners to mitigate their prejudice against the class of majority; (3) Hybrid approaches which consist of the advantages of the two above-mentioned types
THE PROPOSED METHOD The proposed methods based mainly on single-point crossover and jellyfish search to overcome class imbalance classification by resampling the training data. They primarily consist of two stages: first, it rebalances an imbalanced dataset by oversampling algorithm using the single-point crossover to generate the new data of minority classes, second, it finds an optimal subset of the balanced and imbalanced dataset by jellyfish search
Summary
M ACHINE learning (ML) techniques play a vital role in gaining insights from the data in different repositories that are growing exponentially. We introduce in this paper two proposed methods to rebalance an imbalanced dataset by oversampling algorithm using the single-point crossover to generate a new data of minority class and using JS to select the optimal instances from the training set before and after oversampling comparing the results to select the best method to handle the class imbalance problem. THE PROPOSED METHOD The proposed methods based mainly on single-point crossover and jellyfish search to overcome class imbalance classification by resampling the training data They primarily consist of two stages: first, it rebalances an imbalanced dataset by oversampling algorithm using the single-point crossover to generate the new data of minority classes, second, it finds an optimal subset of the balanced and imbalanced dataset by jellyfish search.
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