数据采集与处理2025，Vol.40Issue(3)：711-729,19.DOI:10.16337/j.1004-9037.2025.03.012
针对模相近数据的启发式核密度估计器
Heuristic Kernel Density Estimator for Modal-Proximity Data
摘要
Abstract
Different from the classical probability density estimator construction strategies based on the Parzen window method,we propose a heuristic kernel density estimator(HKDE)based on nearest neighbor error measurement function,to improve the accuracy of fitting probability density function of modal-proximity data.From the perspective of data and model uncertainties,we analyze the defects of traditional kernel density estimators in solving the problem of probability density estimation of modal-proximity data.The heuristic probability density values that can reduce the uncertainty of observed data are obtained by referring to the convergence of probability density values with respect to the histogram box width.Based on the heuristic probability density value,we construct the sophisticated objective function to determine the optimal bandwidth for kernel density estimator by reducing the model uncertainty.Extensive experiments on 18 modal-proximity datasets are conducted to validate the feasibility,rationality and effectiveness of the designed HKDE.Results show that HKDE can obtain a better approximate performance of probability distribution than seven existing representative probability density function estimators.HKDE has lower estimation error and closer probability density function estimates to the real density values than other kernel density estimators.关键词
核密度估计器/模相近观察值/不确定性/启发式概率密度值/直方图箱宽Key words
kernel density estimator(KDE)/modal-proximity data/uncertainty/heuristic probability density value/histogram box width分类
信息技术与安全科学引用本文复制引用
何玉林,陈纯佳,黄哲学,李俊杰,FOURNIER-VIGER Philippe..针对模相近数据的启发式核密度估计器[J].数据采集与处理,2025,40(3):711-729,19.基金项目
广东省自然科学基金面上项目(2023A1515011667) （2023A1515011667）
深圳市基础研究面上项目(JCYJ20210324093609026) （JCYJ20210324093609026）
广东省基础与应用基础研究基金粤深联合基金重点项目(2023B1515120020) （2023B1515120020）
深圳市科技重大专项项目(KJZD20230923114809020). （KJZD20230923114809020）
