随机内核 k-最近邻居回归
Patchanok Srisuradetchai1, Korn Suksrikran1
1Department of Mathematics and Statistics, Thammasat University, Pathum Thani, Thailand.
Frontiers in big data
|July 16, 2024
概括
随机内核 k-最近邻居 (RK-KNN) 回归通过结合内核平滑和引导抽样来改进大数据分析. 这种新的方法提高了预测准确度和模型稳定性,优于标准KNN和R-KNN模型.
科学领域:
- 机器学习 机器学习
- 数据挖掘 数据挖掘
- 统计建模 统计建模
背景情况:
- K-最近邻居 (KNN) 回归是一种非参数方法,有效用于复杂的大数据.
- 然而,KNN容易发生过和不连续性.
- 解决这些局限性对于强大的大数据应用程序至关重要.
研究的目的:
- 介绍大数据的随机内核k-最近邻居 (RK-KNN) 回归.
- 提高预测准确度和模型稳定性.
- 缓解 KNN 固有的过度配套和配套不连续性问题.
主要方法:
- 整合内核光滑与引导抽样.
- 从训练数据中使用随机抽样进行总和预测.
- 选择内核KNN (K-KNN) 的输入变量子集.
主要成果:
- 在15个不同的数据集中,RK-KNN展示了卓越的性能.
- 显著减少了根平均平方误差 (RMSE) 和平均绝对误差 (MAE).
- 与标准 KNN 和随机 KNN (R-KNN) 相比,改进了 R 平方值.
结论:
- 对于大数据,RK-KNN提供了一个强大的,准确的回归方法.
- 该方法有效地解决了KNN的过度装配和不连续性挑战.
- 与最先进的方法进行进一步的比较将验证RK-KNN的有效性.
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