一种改进的可适应变宽核密度估计器  

An improved kernel density estimator with adaptive variable bandwidth

作　　者：金会赏 何玉林 常秀颖[1] 王晓兰 蒋捷[2,3] JIN Huishang;HE Yulin;CHANG Xiuying;WANG Xiaolan;JIANG Jie(Department of Information Engineering,Cangzhou Technical College,Cangzhou 061001,Hebei Province,P.R.China;Big Data Institute,College of Computer Science and Software Engineering,Shenzhen University,Shenzhen 518060,Guangdong Province,P.R.China;National Engineering Laboratory for Big Data System Computing Technology,Shenzhen University,Shenzhen 518060,Guangdong Province,P.R.China)

机构地区：[1]沧州职业技术学院信息工程系,河北沧州061001 [2]深圳大学计算机与软件学院大数据所,广东深圳518060 [3]深圳大学大数据系统计算技术国家工程实验室,广东深圳518060

出　　处：《深圳大学学报（理工版）》2019年第6期709-717,共9页Journal of Shenzhen University(Science and Engineering)

基　　金：国家重点研发计划资助项目(2017YFC0822604-2);中国博士后科学基金资助项目(2016T90799);深圳大学新引进教师科研启动资助项目(2018060)~~

摘　　要：可适应变宽核密度估计器(kernel density estimator with adaptive varying bandwidth,KDE-AVB)是一种基于单个数据点的概率密度估计方法,它以单个数据点为处理对象,利用置信区间交叉法则确定核密度估计器的最优窗口宽度.为加快可适应变宽核密度估计器对最优窗口宽度的寻找,通过引入一种可变的标准差项因子去确定置信区间的上下边界,提出一种改进的可适应变宽核密度估计器(improved kernel density estimator with adaptive varying bandwidth,IKDE-AVB).可变标准差项因子的引入不仅加快了可适应变宽核密度估计器搜索最优窗口宽度的速度,且在一定程度上降低了“过平滑”概率密度估计现象发生的风险.对KDE-AVB和IKDE-AVB的仿真结果表明,IKDE-AVB不仅获得了更快的训练速度(最高降低64%),同时提升了概率密度的估计精度(估计误差最高降低63%).The kernel density estimator with adaptive varying bandwidth(KDE-AVB)estimates the probability density for the given data in a point-wise manner,which determines the optimal bandwidth based on the intersection of confidence intervals(ICI)rule.In order to speed up the search of optimal bandwidth,this paper proposes an improved kernel density estimator with adaptive varying bandwidth(IKDE-AVB)by introducing a flexible standard derivation term factor to calculate the upper and lower boundaries of confidence intervals.The flexible standard derivation term factor not only increases the speed of searching the optimal bandwidth,but also reduces the risk of“over-smoothed”probability density estimation.In comparison with the classical KDE-AVB,IKDE-AVB not only obtains the faster training speed(64%faster at most in training time)but also improves the estimation accuracy of probability density(63%reduction at most in prediction error).The experimental results demonstrate the feasibility and effectiveness of IKDE-AVB.

关 键 词：人工智能 概率密度 核密度估计 可适应变宽 最优窗口宽度 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]