基于空间填充准则的交叉验证方法及其应用  

A Space Filling-Based Cross-Validation Method and Its Applications

作　　者：牟唯嫣 王春玲 赵昕[2] MU Weiyan;WANG Chunling;ZHAO Xin(School of Science,Beijing University of Civil Engineering and Architecture,Beijing 100044;Canvard College,Beijing Technology and Business University,Beijing 101118)

机构地区：[1]北京建筑大学理学院,北京100044 [2]北京工商大学嘉华学院,北京101118

出　　处：《系统科学与数学》2020年第2期382-388,共7页Journal of Systems Science and Mathematical Sciences

基　　金：国家自然科学基金(11671386)资助课题。

摘　　要：在统计学与机器学习中,交叉验证被广泛应用于评估模型的好坏.但交叉验证法的表现一般不稳定,因此评估时通常需要进行多次交叉验证并通过求均值以提高交叉验证算法的稳定性.文章提出了一种基于空间填充准则改进的k折交叉验证方法,它的思想是每一次划分的训练集和测试集均具有较好的均匀性.模拟结果表明,文章所提方法在五种分类模型(k近邻,决策树,随机森林,支持向量机和Adaboost)上对预测精度的估计均比普通k折交叉验证的高.将所提方法应用于骨质疏松实际数据分析中,根据对预测精度的估计选择了最优的模型进行骨质疏松患者的分类预测.In statistics and machine learning,cross-validation is widely used to evaluate the quality of a model.However,the results of cross-validation methods are generally unstable.Therefore,multiple cross-validation is usually required during evaluation and the average value is used to improve the cross-validation algorithm stability.This paper proposes an improved k fold cross-validation method based on the space filling criterion.The idea is that each training and test set divided has better uniformity.The simulation results show that our proposed method estimates the prediction accuracy of five classification models(k nearest neighbor,decision tree,random forest,support vector machine,and Adaboost)than ordinary k-fold cross-validation for prediction accuracy of the estimation is higher.We applied the proposed method to the analysis of actual osteoporosis data,and selected the best model for the classification and prediction of osteoporosis patients based on the estimation of the prediction accuracy.

关 键 词：预测风险 最大距离准则 机器学习 

分 类 号：O212[理学—概率论与数理统计] TP181[理学—数学]