自复制随机森林在缺失值估算中的应用

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Ekaterina Antonenko, Ander Carreño, Jesse Read
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引用次数: 0

摘要

缺失值是数据科学和机器学习中的常见问题。删除缺失值的实例是一种直接的解决方法,但这会严重阻碍后续的数据分析,尤其是当特征数量超过实例数量时。文献中提出了多种方法来弥补缺失值。例如,去噪自动编码器已被有效地用于估算。然而,神经网络方法在较小的数据集上效果相对较差。在这项工作中,我们提出了自复制随机森林(ARF)作为一种多输出学习方法,并在一个可通过迭代或程序过程进行归因的框架中介绍了这种方法。在几个低维和高维数据集上进行的实验表明,ARF 的计算效率很高,与包括神经网络方法在内的竞争对手相比,它的归因性能更好。为了给所提出的缺失值估算框架提供统计分析和数学背景,我们还提出了概率 ARF,即根据不同的估算假设提供置信度值,从而最大限度地提高这种框架在以预测性能为目标的机器学习管道中的效用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Autoreplicative random forests with applications to missing value imputation

Autoreplicative random forests with applications to missing value imputation

Missing values are a common problem in data science and machine learning. Removing instances with missing values is a straightforward workaround, but this can significantly hinder subsequent data analysis, particularly when features outnumber instances. There are a variety of methodologies proposed in the literature for imputing missing values. Denoising Autoencoders, for example, have been leveraged efficiently for imputation. However, neural network approaches have been relatively less effective on smaller datasets. In this work, we propose Autoreplicative Random Forests (ARF) as a multi-output learning approach, which we introduce in the context of a framework that may impute via either an iterative or procedural process. Experiments on several low- and high-dimensional datasets show that ARF is computationally efficient and exhibits better imputation performance than its competitors, including neural network approaches. In order to provide statistical analysis and mathematical background to the proposed missing value imputation framework, we also propose probabilistic ARFs, where the confidence values are provided over different imputation hypotheses, therefore maximizing the utility of such a framework in a machine-learning pipeline targeting predictive performance.

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来源期刊
Machine Learning
Machine Learning 工程技术-计算机:人工智能
CiteScore
11.00
自引率
2.70%
发文量
162
审稿时长
3 months
期刊介绍: Machine Learning serves as a global platform dedicated to computational approaches in learning. The journal reports substantial findings on diverse learning methods applied to various problems, offering support through empirical studies, theoretical analysis, or connections to psychological phenomena. It demonstrates the application of learning methods to solve significant problems and aims to enhance the conduct of machine learning research with a focus on verifiable and replicable evidence in published papers.
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