An introduction to R package `mvs`
Abstract
In biomedical science, a set of objects or persons can often be described by
multiple distinct sets of features obtained from different data sources or
modalities (called "multi-view data"). Classical machine learning methods
ignore the multi-view structure of such data, limiting model interpretability
and performance. The R package `mvs` provides methods that were designed
specifically for dealing with multi-view data, based on the multi-view stacking
(MVS) framework. MVS is a form of supervised (machine) learning used to train
multi-view classification or prediction models. MVS works by training a
learning algorithm on each view separately, estimating the predictive power of
each view-specific model through cross-validation, and then using another
learning algorithm to assign weights to the view-specific models based on their
estimated predictions. MVS is a form of ensemble learning, dividing the large
multi-view learning problem into smaller sub-problems. Most of these
sub-problems can be solved in parallel, making it computationally attractive.
Additionally, the number of features of the sub-problems is greatly reduced
compared with the full multi-view learning problem. This makes MVS especially
useful when the total number of features is larger than the number of
observations (i.e., high-dimensional data). MVS can still be applied even if
the sub-problems are themselves high-dimensional by adding suitable penalty
terms to the learning algorithms. Furthermore, MVS can be used to automatically
select the views which are most important for prediction. The R package `mvs`
makes fitting MVS models, including such penalty terms, easily and openly
accessible. `mvs` allows for the fitting of stacked models with any number of
levels, with different penalty terms, different outcome distributions, and
provides several options for missing data handling.引用本文复制引用
Wouter van Loon.An introduction to R package `mvs`[EB/OL].(2025-04-24)[2026-01-08].https://arxiv.org/abs/2504.17546.学科分类
生物科学研究方法、生物科学研究技术
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