ArticleBMC bioinformatics2017
CURE-SMOTE algorithm and hybrid algorithm for feature selection and parameter optimization based on random forests.
Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning (ML) techniques to predict breast cancer in imbalanced datasets: a systematic review.Journal of cancer survivorship : research and practice · 2025Pooled it
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- Machine learning-enabled prediction of prolonged length of stay in hospital after surgery for tuberculosis spondylitis patients with unbalanced data: a novel approach using explainable artificial intelligence (XAI).European journal of medical research · 2024Article
- A cluster-based SMOTE both-sampling (CSBBoost) ensemble algorithm for classifying imbalanced data.Scientific reports · 2024Article
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- Prediction Model for Infectious Disease Health Literacy Based on Synthetic Minority Oversampling Technique Algorithm.Computational and mathematical methods in medicine · 2022Article
- Research on expansion and classification of imbalanced data based on SMOTE algorithm.Scientific reports · 2021Article
- Targeting Hypertension Screening in Low- and Middle-Income Countries: A Cross-Sectional Analysis of 1.2 Million Adults in 56 Countries.Journal of the American Heart Association · 2021Article
- Development of a Hierarchical Support Vector Regression-Based In Silico Model for Caco-2 Permeability.Pharmaceutics · 2021Article
- A Hybrid Method to Predict Postoperative Survival of Lung Cancer Using Improved SMOTE and Adaptive SVM.Computational and mathematical methods in medicine · 2021Article
- Outlier-SMOTE: A refined oversampling technique for improved detection of COVID-19.Intelligence-based medicine · 2020Article
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2 authors.
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Abstract
backgroundThe random forests algorithm is a type of classifier with prominent universality, a wide application range, and robustness for avoiding overfitting. But there are still some drawbacks to random forests. Therefore, to improve the performance of random forests, this paper seeks to improve imbalanced data processing, feature selection and parameter optimization.
resultsWe propose the CURE-SMOTE algorithm for the imbalanced data classification problem. Experiments on imbalanced UCI data reveal that the combination of Clustering Using Representatives (CURE) enhances the original synthetic minority oversampling technique (SMOTE) algorithms effectively compared with the classification results on the original data using random sampling, Borderline-SMOTE1, safe-level SMOTE, C-SMOTE, and k-means-SMOTE. Additionally, the hybrid RF (random forests) algorithm has been proposed for feature selection and parameter optimization, which uses the minimum out of bag (OOB) data error as its objective function. Simulation results on binary and higher-dimensional data indicate that the proposed hybrid RF algorithms, hybrid genetic-random forests algorithm, hybrid particle swarm-random forests algorithm and hybrid fish swarm-random forests algorithm can achieve the minimum OOB error and show the best generalization ability.
conclusionThe training set produced from the proposed CURE-SMOTE algorithm is closer to the original data distribution because it contains minimal noise. Thus, better classification results are produced from this feasible and effective algorithm. Moreover, the hybrid algorithm's F-value, G-mean, AUC and OOB scores demonstrate that they surpass the performance of the original RF algorithm. Hence, this hybrid algorithm provides a new way to perform feature selection and parameter optimization.
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