MissForest-non-parametric missing value imputation for mixed-type data
MissForest-non-parametric missing value imputation for mixed-type data
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DOI：
10.1093/bioinformatics/btr597
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发表时间：
2012-01-01
期刊：
影响因子：
5.8
通讯作者：
Buehlmann, Peter
中科院分区：
文献类型：
--
作者：
Stekhoven, Daniel J.;Buehlmann, Peter
动机：基于高通量技术的现代数据采集经常面临数据缺失的问题。在分析这种大规模数据时通常使用的算法往往依赖于一套完整的集合。缺失值推算为这一问题提供了解决方案。然而，大多数可用的推算方法仅限于一种类型的变量：连续变量或分类变量。对于混合类型的数据，不同的类型通常是单独处理的。因此，这些方法忽略了变量类型之间可能存在的关系。我们提出了一种能够同时处理不同类型变量的非参数方法。结果：我们比较了几种最新的缺失值填补方法。提出并评价了一种基于随机森林的迭代补偿方法(MISS森林)。通过对许多未修剪的分类或回归树进行平均，随机森林本质上构成了一种多重分配方案。利用随机森林的内置袋装误差估计，我们能够在不需要测试集的情况下估计补偿误差。对来自不同生物领域的多个数据集进行评估，其中人工引入的缺失值从10%到30%不等。我们展示了MissForest可以成功地处理缺失值，特别是在包含不同类型变量的数据集中。在我们的比较研究中，MissForest优于其他补偿方法，特别是在怀疑复杂交互和非线性关系的数据环境中。事实证明，MissForest的现成归罪误差估计在所有情况下都是足够的。此外，MissForest还显示出诱人的计算效率，并且能够处理高维数据。
Motivation: Modern data acquisition based on high-throughput technology is often facing the problem of missing data. Algorithms commonly used in the analysis of such large-scale data often depend on a complete set. Missing value imputation offers a solution to this problem. However, the majority of available imputation methods are restricted to one type of variable only: continuous or categorical. For mixed-type data, the different types are usually handled separately. Therefore, these methods ignore possible relations between variable types. We propose a non-parametric method which can cope with different types of variables simultaneously.Results: We compare several state of the art methods for the imputation of missing values. We propose and evaluate an iterative imputation method (missForest) based on a random forest. By averaging over many unpruned classification or regression trees, random forest intrinsically constitutes a multiple imputation scheme. Using the built-in out-of-bag error estimates of random forest, we are able to estimate the imputation error without the need of a test set. Evaluation is performed on multiple datasets coming from a diverse selection of biological fields with artificially introduced missing values ranging from 10% to 30%. We show that missForest can successfully handle missing values, particularly in datasets including different types of variables. In our comparative study, missForest outperforms other methods of imputation especially in data settings where complex interactions and non-linear relations are suspected. The out-of-bag imputation error estimates of missForest prove to be adequate in all settings. Additionally, missForest exhibits attractive computational efficiency and can cope with high-dimensional data.