BEST: a decision tree algorithm that handles missing values

被引：21

作者：

Beaulac, Cedric ^{[1
]}

Rosenthal, Jeffrey S. ^{[1
]}

机构：

[1] Univ Toronto, Dept Stat Sci, Toronto, ON, Canada

来源：

COMPUTATIONAL STATISTICS | 2020年 / 35卷 / 03期

基金：

加拿大自然科学与工程研究理事会;

关键词：

Classification and regression tree; Missing data; Applied machine learning; Interpretable models; Variable importance analysis; CLASSIFICATION TREES; IMPUTATION; INFERENCE;

D O I：

10.1007/s00180-020-00987-z

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

The main contribution of this paper is the development of a new decision tree algorithm. The proposed approach allows users to guide the algorithm through the data partitioning process. We believe this feature has many applications but in this paper we demonstrate how to utilize this algorithm to analyse data sets containing missing values. We tested our algorithm against simulated data sets with various missing data structures and a real data set. The results demonstrate that this new classification procedure efficiently handles missing values and produces results that are slightly more accurate and more interpretable than most common procedures without any imputations or pre-processing.

引用

页码：1001 / 1026

页数：26

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