Random Forest Missing Data Algorithms.

IF 2.1 4区数学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Statistical Analysis and Data Mining Pub Date : 2017-12-01 Epub Date: 2017-06-13 DOI:10.1002/sam.11348

Fei Tang, Hemant Ishwaran

引用次数: 377

Abstract

Random forest (RF) missing data algorithms are an attractive approach for imputing missing data. They have the desirable properties of being able to handle mixed types of missing data, they are adaptive to interactions and nonlinearity, and they have the potential to scale to big data settings. Currently there are many different RF imputation algorithms, but relatively little guidance about their efficacy. Using a large, diverse collection of data sets, imputation performance of various RF algorithms was assessed under different missing data mechanisms. Algorithms included proximity imputation, on the fly imputation, and imputation utilizing multivariate unsupervised and supervised splitting-the latter class representing a generalization of a new promising imputation algorithm called missForest. Our findings reveal RF imputation to be generally robust with performance improving with increasing correlation. Performance was good under moderate to high missingness, and even (in certain cases) when data was missing not at random.

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随机森林缺失数据算法。

随机森林缺失数据算法是一种很有吸引力的缺失数据输入方法。它们具有能够处理混合类型缺失数据的理想特性，它们能够适应相互作用和非线性，并且具有扩展到大数据设置的潜力。目前有许多不同的射频插补算法，但对其有效性的指导相对较少。利用大量不同的数据集，在不同的缺失数据机制下评估了各种RF算法的插补性能。算法包括接近输入、动态输入和利用多变量无监督和监督分割的输入——后者代表了一种新的有前途的输入算法misforest的推广。我们的研究结果表明，射频插补通常是稳健的，性能随着相关性的增加而提高。在中度到高度丢失的情况下，甚至(在某些情况下)数据不是随机丢失的情况下，性能都很好。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Statistical Analysis and Data Mining COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

CiteScore

3.20

自引率

7.70%

发文量

期刊介绍： Statistical Analysis and Data Mining addresses the broad area of data analysis, including statistical approaches, machine learning, data mining, and applications. Topics include statistical and computational approaches for analyzing massive and complex datasets, novel statistical and/or machine learning methods and theory, and state-of-the-art applications with high impact. Of special interest are articles that describe innovative analytical techniques, and discuss their application to real problems, in such a way that they are accessible and beneficial to domain experts across science, engineering, and commerce. The focus of the journal is on papers which satisfy one or more of the following criteria: Solve data analysis problems associated with massive, complex datasets Develop innovative statistical approaches, machine learning algorithms, or methods integrating ideas across disciplines, e.g., statistics, computer science, electrical engineering, operation research. Formulate and solve high-impact real-world problems which challenge existing paradigms via new statistical and/or computational models Provide survey to prominent research topics.