An automatic sampling ratio detection method based on genetic algorithm for imbalanced data classification

Zheng, Ming; Li, Tong; Sun, Liping; Wang, Taochun; Jie, Biao; Yang, Wei; Tang, Mingjing; Lv, Changlong

doi:10.1016/j.knosys.2021.106800

Cited by 19 publications

(8 citation statements)

References 37 publications

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“…The hybrid model (GNRCS) after treating the imbalance in the dataset resulted in less set of rules and better execution time (Zheng et al. 2021 ).…”

Section: Resultsmentioning

confidence: 99%

“…The classification task in NRCS is improved in GNRCS using GA (Zheng et al. 2021 ; Mello-Romn and Hernandez 2020 ; Qiao et al. 2021 ; Pourrajabian et al.…”

Section: Methodsmentioning

confidence: 99%

“…Many proposed methods deal with imbalanced data, such as (Zheng et al. 2021 ; He and Garcia 2009 ; Sun et al. 2007 ; Tharwat and Gabel 2020 ).…”

Section: Methodsmentioning

confidence: 99%

“…We used the Michigan approach. The classification task in NRCS is improved in GNRCS using GA (Zheng et al 2021;Mello-Romn and Hernandez 2020;Qiao et al 2021;Pourrajabian et al 2021;Kukker and Sharma 2021) to produce the best "If-Then" rules and remove the redundant ones. Algorithm 1 gives a summary of the GNRCS steps and shows the main phases of the proposed GNRCS model.…”

Section: Bio-inspired-based Rule Reduction Phasementioning

confidence: 99%

“…This results in bias in classification toward the majority class and increases the misclassification rate of the minority class. Many proposed methods deal with imbalanced data, such as (Zheng et al 2021;He and Garcia 2009;Sun et al 2007;Tharwat and Gabel 2020). There are three famous sampling methods (He and Garcia 2009).…”

Section: Sampling Techniques For Imbalanced Data Treatmentmentioning

confidence: 99%

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RETRACTED ARTICLE: Hybrid intelligent model for classifying chest X-ray images of COVID-19 patients using genetic algorithm and neutrosophic logic

et al. 2021

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The highly spreading virus, COVID-19, created a huge need for an accurate and speedy diagnosis method. The famous RT-PCR test is costly and not available for many suspected cases. This article proposes a neurotrophic model to diagnose COVID-19 patients based on their chest X-ray images. The proposed model has five main phases. First, the speeded up robust features (SURF) method is applied to each X-ray image to extract robust invariant features. Second, three sampling algorithms are applied to treat imbalanced dataset. Third, the neutrosophic rule-based classification system is proposed to generate a set of rules based on the three neutrosophic values < T; I; F>, the degrees of truth, indeterminacy falsity. Fourth, a genetic algorithm is applied to select the optimal neutrosophic rules to improve the classification performance. Fifth, in this phase, the classification-based neutrosophic logic is proposed. The testing rule matrix is constructed with no class label, and the goal of this phase is to determine the class label for each testing rule using intersection percentage between testing and training rules. The proposed model is referred to as GNRCS. It is compared with six state-of-the-art classifiers such as multilayer perceptron (MLP), support vector machines (SVM), linear discriminant analysis (LDA), decision tree (DT), naive Bayes (NB), and random forest classifiers (RFC) with quality measures of accuracy, precision, sensitivity, specificity, and F1-score. The results show that the proposed model is powerful for COVID-19 recognition with high specificity and high sensitivity and less computational complexity. Therefore, the proposed GNRCS model could be used for real-time automatic early recognition of COVID-19.

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“…The hybrid model (GNRCS) after treating the imbalance in the dataset resulted in less set of rules and better execution time (Zheng et al. 2021 ).…”

Section: Resultsmentioning

confidence: 99%

“…The classification task in NRCS is improved in GNRCS using GA (Zheng et al. 2021 ; Mello-Romn and Hernandez 2020 ; Qiao et al. 2021 ; Pourrajabian et al.…”

Section: Methodsmentioning

confidence: 99%

“…Many proposed methods deal with imbalanced data, such as (Zheng et al. 2021 ; He and Garcia 2009 ; Sun et al. 2007 ; Tharwat and Gabel 2020 ).…”

Section: Methodsmentioning

confidence: 99%