EBSMOTE: Evaluation-Based Synthetic Minority Oversampling TEchnique for Imbalanced Dataset Learning

Hussein, Ahmed Saad; Diallo, Bassoma; Liu, Jia

doi:10.1109/iske47853.2019.9170367

Cited by 1 publication

(3 citation statements)

References 23 publications

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“…Authors in [2] proposed a novel hybrid algorithm named Ant Colony Optimization Resampling (ACOR) to improve the class imbalance classification. In their algorithm four traditional oversampling methods -SMOTE [9], BSO [16], ROS [36], and ADASYN [1]-had been used first to rebalance the imbalanced datasets; subsequently, the ACO algorithm, had been applied to the resampled datasets to find an optimal subset from the obtained balanced training dataset. To validate their results, they used three classifiers naive Bayes [10], C4.5 [11], and support vector machine with radial basis function kernel (RBF-SVM) classifier [13].…”

Section: Comparitave Analysismentioning

confidence: 99%

“…To obtain the world's maximum data, many companies generate large data centres there. The data becomes useable if ML methods analyze them and some decision-making results are generated [1]. In many applications in the real world, distorted sample distribution affect the classification process as instances of some classes appear rarely.…”

Section: Introductionmentioning

confidence: 99%

“…Comparison between the performance of the proposed method and best results of state-of-the-art methods in[1] …”

mentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Comparitave Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%