Adaptive Multi-objective Swarm Crossover Optimization for Imbalanced Data Classification

Li, Jinyan; Fong, Simon; Meng, Yuan; Wong, Raymond K.

doi:10.1007/978-3-319-49586-6_25

Cited by 8 publications

(3 citation statements)

References 29 publications

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“…Li et al [28] presented a unified pre-processing method utilizing stochastic swarm heuristics to jointly optimize the mixtures from the two classes by gradually reconstructing the training dataset. Their method exhibited competitive performance in comparison with popular techniques.…”

Section: Literature Reviewmentioning

confidence: 99%

An Improved Hybrid Approach for Handling Class Imbalance Problem

Desuky

Hussain

2021

Arab J Sci Eng

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Class imbalance issue that presents in many real-world datasets exhibit favouritism toward the majority class and showcases poor performance for the minority class. Such misclassifications may incur dubious outcome in case of disease diagnosis and other critical applications. Hence, it is a hot topic for the researchers to tackle the class imbalance issue. We present a novel hybrid approach for handling such datasets. We utilize simulated annealing algorithm for undersampling and apply support vector machine, decision tree, k-nearest neighbor and discriminant analysis for the classification task. We validate our technique in 51 real-world datasets and compare it with other recent works. Our technique yields better efficacy than the existing techniques and hence it can be applied in imbalance datasets to mitigate the misclassification.

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Section: Literature Reviewmentioning

confidence: 99%

An Improved Hybrid Approach for Handling Class Imbalance Problem

Desuky

Hussain

2021

Arab J Sci Eng

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“…4 illustrate this situation. Therefore, there is non-unique global best solution and the suitable solutions are all recorded in a solution set, it is called non-inferior set or Pareto optimal set [40,41]. This set contains the solutions with one of the following conditions: 1. the solution has better performances for achieving dual objectives; 2. the solution could improve one objective and the other one does not degrade outside the range (1.0e-4).…”

Section: Multi-objective Problem In Imbalance Classificationmentioning

confidence: 99%

“…This set contains the solutions with one of the following conditions: 1. the solution has better performances for achieving dual objectives; 2. the solution could improve one objective and the other one does not degrade outside the range (1.0e-4). The decision marking of our experiment is to select a solution which produces the best Kappa statistics and Accuracy as the final results from the non-inferior set [40].…”

Section: Multi-objective Problem In Imbalance Classificationmentioning

confidence: 99%

A binary PSO-based ensemble under-sampling model for rebalancing imbalanced training data

Fong

et al. 2021

J Supercomput

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Ensemble technique and under-sampling technique are both effective tools used for imbalanced dataset classification problems. In this paper, a novel ensemble method combining the advantages of both ensemble learning for biasing classifiers and a new under-sampling method is proposed. The under-sampling method is named Binary PSO instance selection; it gathers with ensemble classifiers to find the most suitable length and combination of the majority class samples to build a new dataset with minority class samples. The proposed method adopts multi-objective strategy, and contribution of this method is a notable improvement of the performances of imbalanced classification, and in the meantime guaranteeing a best integrity possible for the original dataset. We experimented the proposed method and compared its performance of processing imbalanced datasets with several other conventional basic ensemble methods. Experiment is also conducted on these imbalanced datasets using an improved version where ensemble classifiers are wrapped in the Binary PSO instance selection. According to experimental results, our proposed methods outperform single ensemble methods, state-of-the-art under-sampling methods, and also combinations of these methods with the traditional PSO instance selection algorithm.

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Benchmarking Swarm Rebalancing Algorithm for Relieving Imbalanced Machine Learning Problems

Fong

2018

International Series on Computer Entertainment and Media Technology

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Adaptive Multi-objective Swarm Crossover Optimization for Imbalanced Data Classification

Cited by 8 publications

References 29 publications

An Improved Hybrid Approach for Handling Class Imbalance Problem

An Improved Hybrid Approach for Handling Class Imbalance Problem

A binary PSO-based ensemble under-sampling model for rebalancing imbalanced training data

Benchmarking Swarm Rebalancing Algorithm for Relieving Imbalanced Machine Learning Problems

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