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Random Kernel k-nearest neighbors (RK-KNN) regression improves big data analysis by combining kernel smoothing and bootstrap sampling. This novel method enhances prediction accuracy and model robustness, outperforming standard KNN and R-KNN models.

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bootstrappingfeature selectionk-nearest neighbors regressionkernel k-nearest neighborsstate-of-the-art (SOTA)

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Area of Science:

  • Machine Learning
  • Data Mining
  • Statistical Modeling

Background:

  • K-nearest neighbors (KNN) regression is a nonparametric method effective for complex, big data.
  • However, KNN is prone to overfitting and fit discontinuity.
  • Addressing these limitations is crucial for robust big data applications.

Purpose of the Study:

  • Introduce Random Kernel k-nearest neighbors (RK-KNN) regression for big data.
  • Enhance prediction accuracy and model robustness.
  • Mitigate overfitting and fit discontinuity issues inherent in KNN.

Main Methods:

  • Integrate kernel smoothing with bootstrap sampling.
  • Aggregate predictions using random sampling from training data.
  • Select input variable subsets for kernel KNN (K-KNN).

Main Results:

  • RK-KNN demonstrated superior performance across 15 diverse datasets.
  • Significantly reduced Root Mean Square Error (RMSE) and Mean Absolute Error (MAE).
  • Improved R-squared values compared to standard KNN and Random KNN (R-KNN).

Conclusions:

  • RK-KNN offers a robust and accurate regression approach for big data.
  • The method effectively addresses KNN's overfitting and discontinuity challenges.
  • Further benchmarking against state-of-the-art methods will validate RK-KNN's efficacy.