ArticleFrontiers in big data2024
Random kernel k-nearest neighbors regression.
Article in Frontiers in big data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- The Relationship between Biological Aging and Cognitive Function: A Machine Learning Model.Yonsei medical journal · 2026Article
- Integrating ESG indicators and behavioral factors to estimate the mental health index in Italy.International journal of mental health systems · 2026Article
- Optimal machine learning models and factors for global infectious diseases: An ecological analysis.Infectious medicine · 2026Article
- Adaptive regression model for Parkinson's disease diagnosis from speech signals using Box-Cox-based clustering and extremely randomization.Scientific reports · 2026Article
- DTF-STCANet: A Dual Time-Frequency Swin Transformer and ConvNeXt Attention Network for Heart Sound Classification.Diagnostics (Basel, Switzerland) · 2026Article
- Machine learning prediction of ACL loading during the wide lunge: a multifactorial coupling analysis based on kinematic and electromyographic signals.Frontiers in bioengineering and biotechnology · 2026Article
- Hybrid experimental and machine learning approach for optimizing abrasive wear of microcrystalline cellulose modified hemp/bamboo fiber composites.Scientific reports · 2025Article
- Interpretable ensemble remaining useful life prediction enables dynamic maintenance scheduling for aircraft engines.Scientific reports · 2025Article
- Structure-based machine learning screening identifies natural product candidates as potential geroprotectors.Journal of cheminformatics · 2025Article
- Machine Learning-Based Non-Invasive Prediction of Metabolic Dysfunction-Associated Steatohepatitis in Obese Patients: A Retrospective Study.Diagnostics (Basel, Switzerland) · 2025Article
- Decision tree model for predicting ovarian tumor malignancy based on clinical markers and preoperative circulating blood cells.BMC medical informatics and decision making · 2025Article
- Interpretable machine learning models based on multi-dimensional fusion data for predicting positive surgical margins in robot-assisted radical prostatectomy: a retrospective study.Frontiers in oncology · 2025Article
- A machine learning-based predictive model for complication risks in vacuum-assisted breast biopsy.Frontiers in surgery · 2025Article
- The application of machine learning in clinical microbiology and infectious diseases.Frontiers in cellular and infection microbiology · 2025Review
- Machine learning combining external validation to explore the immunopathogenesis of diabetic foot ulcer and predict therapeutic drugs.PloS one · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The k-nearest neighbors (KNN) regression method, known for its nonparametric nature, is highly valued for its simplicity and its effectiveness in handling complex structured data, particularly in big data contexts. However, this method is susceptible to overfitting and fit discontinuity, which present significant challenges. This paper introduces the random kernel k-nearest neighbors (RK-KNN) regression as a novel approach that is well-suited for big data applications. It integrates kernel smoothing with bootstrap sampling to enhance prediction accuracy and the robustness of the model. This method aggregates multiple predictions using random sampling from the training dataset and selects subsets of input variables for kernel KNN (K-KNN). A comprehensive evaluation of RK-KNN on 15 diverse datasets, employing various kernel functions including Gaussian and Epanechnikov, demonstrates its superior performance. When compared to standard KNN and the random KNN (R-KNN) models, it significantly reduces the root mean square error (RMSE) and mean absolute error, as well as improving R-squared values. The RK-KNN variant that employs a specific kernel function yielding the lowest RMSE will be benchmarked against state-of-the-art methods, including support vector regression, artificial neural networks, and random forests.
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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.