ArticleBioData mining2013
LVQ-SMOTE - Learning Vector Quantization based Synthetic Minority Over-sampling Technique for biomedical data.
Article in BioData mining, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers.
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65 citing papers in PubMed.
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- Longitudinal variability of CT imaging features for predicting pulmonary nodule invasiveness: A multicenter study.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2025Article
- Multiparametric magnetic resonance imaging of deep learning-based super-resolution reconstruction for predicting histopathologic grade in hepatocellular carcinoma.World journal of gastroenterology · 2025Article
- A Decision Tree Model to Help Treatment Decision-Making for Unruptured Intracranial Aneurysms: A Multi-center, Long-Term Follow-up Study in a Large Chinese Cohort.Translational stroke research · 2025Observational
- Contrast-enhanced mammography-based interpretable machine learning model for the prediction of the molecular subtype breast cancers.BMC medical imaging · 2025Article
- Evaluating machine learning algorithms for predicting HIV status among young Thai men who have sex with men.BMJ health & care informatics · 2025Article
- Application of machine learning for the analysis of peripheral blood biomarkers in oral mucosal diseases: a cross-sectional study.BMC oral health · 2025Article
- Inferring kinase-phosphosite regulation from phosphoproteome-enriched cancer multi-omics datasets.Briefings in bioinformatics · 2025Article
- Predicting Neoplastic Polyp in Patients With Gallbladder Polyps Using Interpretable Machine Learning Models: Retrospective Cohort Study.Cancer medicine · 2025Article
- Developing practical machine learning survival models to identify high-risk patients for in-hospital mortality following traumatic brain injury.Scientific reports · 2025Article
- Web-based machine learning application for interpretable prediction of prolonged length of stay after lumbar spinal stenosis surgery: a retrospective cohort study with explainable AI.Frontiers in physiology · 2025Article
- Predicting symptomatic kidney stones using machine learning algorithms: insights from the Fasa adults cohort study (FACS).BMC research notes · 2024Article
- Processing imbalanced medical data at the data level with assisted-reproduction data as an example.BioData mining · 2024Article
- Comprehensive cross cancer analyses reveal mutational signature cancer specificity.Quantitative biology (Beijing, China) · 2024Article
- 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
- Machine learning models for predicting of PD-1 treatment efficacy in Pan-cancer patients based on routine hematologic and biochemical parameters.Cancer cell international · 2024Article
- Article
- Deep learning model integrating radiologic and clinical data to predict mortality after ischemic stroke.Heliyon · 2024Article
- MR-based radiomics predictive modelling of EGFR mutation and HER2 overexpression in metastatic brain adenocarcinoma: a two-centre study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
5 more citing papers are in PubMed but not listed here.
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4 authors.
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Abstract
backgroundOver-sampling methods based on Synthetic Minority Over-sampling Technique (SMOTE) have been proposed for classification problems of imbalanced biomedical data. However, the existing over-sampling methods achieve slightly better or sometimes worse result than the simplest SMOTE. In order to improve the effectiveness of SMOTE, this paper presents a novel over-sampling method using codebooks obtained by the learning vector quantization. In general, even when an existing SMOTE applied to a biomedical dataset, its empty feature space is still so huge that most classification algorithms would not perform well on estimating borderlines between classes. To tackle this problem, our over-sampling method generates synthetic samples which occupy more feature space than the other SMOTE algorithms. Briefly saying, our over-sampling method enables to generate useful synthetic samples by referring to actual samples taken from real-world datasets.
resultsExperiments on eight real-world imbalanced datasets demonstrate that our proposed over-sampling method performs better than the simplest SMOTE on four of five standard classification algorithms. Moreover, it is seen that the performance of our method increases if the latest SMOTE called MWMOTE is used in our algorithm. Experiments on datasets for β-turn types prediction show some important patterns that have not been seen in previous analyses.
conclusionsThe proposed over-sampling method generates useful synthetic samples for the classification of imbalanced biomedical data. Besides, the proposed over-sampling method is basically compatible with basic classification algorithms and the existing over-sampling methods.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.