基于无放回抽样的帕尔森窗口集成方法

Sampling without replacement-based Parzen window ensemble method

为解决大规模数据集的概率密度函数估计问题,提出一种基于无放回抽样的帕尔森窗口集成(sampling without replacement-based Parzen window ensemble,SR-PWE)方法,该方法在不需要利用全部数据的前提下,能够以较低的计算复杂度获得令人满意的概率密度函数估计表现.基于无放回抽样得到的若干原数据集的数据子集,利用帕尔森窗口法在数据子集上进行基概率密度函数估计,并将抽样上估计的基概率密度函数集成得到原始数据集的概率密度函数.通过在柯西分布和正态分布上对比帕尔森窗口法和SRPWE方法的概率密度函数估计表现,证实SR-PWE方法可行且有效.

Although the Parzen window method is a classical probability density function(PDF)estimation method,which is widely applied in the fields of machine learning and pattern recognition,it is unsuitable for the PDF estimation of large-scale data because of its high computational complexity and bandwidth sensibility.In this paper,to handle the PDF estimation for large-scale data,we propose a sampling without replacement-based Parzen window ensemble(SR-PWE)method which conducts the PDF estimation based on the partial data and is able to obtain the satisfactory PDF estimation performance with the low computation complexity.Firstly,we generate a number of sub-datasets from the original data set by sampling without replacement.Secondly,we estimate the base PDFs by using the Parzen window method on these sub-datasets.Then,we determine the PDF of original data set based on the fusion of base PDFs.Finally,the experimental results on Cauchy and normal distributions demonstrate the feasibility and effectiveness of sampling without replacement-based Parzen window ensemble method.