Multi-view stacking is a framework for combining information from different views (i.e. different feature sets) describing the same set of objects. In this framework, a base-learner algorithm is trained on each view separately, and their predictions are then combined by a meta-learner algorithm. In a previous study, stacked penalized logistic regression, a special case of multi-view stacking, has been shown to be useful in identifying which views are most important for prediction. In this article we expand this research by considering seven different algorithms to use as the meta-learner, and evaluating their view selection and classification performance in simulations and two applications on real gene-expression data sets. Our results suggest that if both view selection and classification accuracy are important to the research at hand, then the nonnegative lasso, nonnegative adaptive lasso and nonnegative elastic net are suitable meta-learners. Exactly which among these three is to be preferred depends on the research context. The remaining four meta-learners, namely nonnegative ridge regression, nonnegative forward selection, stability selection and the interpolating predictor, show little advantages in order to be preferred over the other three.

翻译：多视角堆叠是一种整合描述同一组对象的不同视角（即不同特征集）信息的框架。在此框架中，每个视角分别训练一个基学习器算法，然后通过元学习器算法整合其预测结果。先前研究表明，作为多视角堆叠特例的堆叠惩罚逻辑回归，能有效识别对预测最重要的视角。本文在此基础上扩展研究，考虑七种不同算法作为元学习器，通过模拟实验和两个真实基因表达数据集的应用场景，评估其视角选择与分类性能。结果表明：若视角选择与分类精度对当前研究同等重要，非负拉索、非负自适应拉索和非负弹性网络是合适的元学习器；这三者中的具体选择取决于研究情境。其余四种元学习器（非负岭回归、非负前向选择、稳定性选择及插值预测器）相比前三者并未显示出显著优势。