ArticleScientific reports2024
A cluster-based SMOTE both-sampling (CSBBoost) ensemble algorithm for classifying imbalanced data.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
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- Generative adversarial networks and hyperparameter-optimized XGBoost for enhanced heart disease prediction.Scientific reports · 2026Article
- An adaptive data rebalancing framework for real-time traffic risk prediction.Scientific reports · 2026Article
- A risk prediction model for recurrence in patients with borderline ovarian tumor based on artificial neural network: development and validation study.Journal of ovarian research · 2025Article
- Computational prediction of mutagenicity through comprehensive cell painting analysis.Mutagenesis · 2025Article
- Comparative analysis of algorithmic approaches in ensemble learning: bagging vs. boosting.Scientific reports · 2025Article
- Ki-67 as a Predictor of Metastasis in Adrenocortical Carcinoma: Artificial Intelligence Insights from Retrospective Imaging Data.Journal of clinical medicine · 2025Article
- SMOTE algorithm optimization and application in corporate credit risk prediction with diversification strategy consideration.Scientific reports · 2025Article
- A Perspective on Artificial Intelligence for Molecular Pathologists.The Journal of molecular diagnostics : JMD · 2025Review
- Optimizing Rare Disease Gait Classification through Data Balancing and Generative AI: Insights from Hereditary Cerebellar Ataxia.Sensors (Basel, Switzerland) · 2024Article
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2 authors.
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Abstract
In this paper, a Cluster-based Synthetic minority oversampling technique (SMOTE) Both-sampling (CSBBoost) ensemble algorithm is proposed for classifying imbalanced data. In this algorithm, a combination of over-sampling, under-sampling, and different ensemble algorithms, including Extreme Gradient Boosting (XGBoost), random forest, and bagging, is employed in order to achieve a balanced dataset and address the issues including redundancy of data after over-sampling, information loss in under-sampling, and random sample selection for sampling and sample generation. The performance of the proposed algorithm is evaluated and compared to different state-of-the-art competing algorithms based on 20 benchmark imbalanced datasets in terms of the harmonic mean of precision and recall (F1) and area under the receiver operating characteristics curve (AUC) measures. Based on the results, the proposed CSBBoost algorithm performs significantly better than the competing algorithms. In addition, a real-world dataset is used to demonstrate the applicability of the proposed algorithm.
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Registered trials
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