The features in some machine learning datasets can naturally be divided into groups. This is the case with genomic data, where features can be grouped by chromosome. In many applications it is common for these groupings to be ignored, as interactions may exist between features belonging to different groups. However, including a group that does not influence a response introduces noise when fitting a model, leading to suboptimal predictive accuracy. Here we present two general frameworks for the generation and combination of meta-features when feature groupings are present. Furthermore, we make comparisons to multi-target learning, given that one is typically interested in predicting multiple phenotypes. We evaluated the frameworks and multi-target learning approaches on a genomic rice dataset where the regression task is to predict plant phenotype. Our results demonstrate that there are use cases for both the meta and multi-target approaches, given that overall, they significantly outperform the base case.

中文翻译：

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用元和多目标学习预测水稻表型

一些机器学习数据集中的特征自然可以分组。基因组数据就是这种情况，其中的特征可以按染色体分组。在许多应用程序中，通常会忽略这些分组，因为属于不同组的特征之间可能存在交互。但是，在拟合模型时，包含一个不影响响应的组会引入噪声，从而导致预测准确性欠佳。在这里，我们提出了两种通用框架，用于在存在特征分组时生成和组合元特征。此外，我们与多目标学习进行了比较，因为人们通常对预测多种表型感兴趣。我们在基因组水稻数据集上评估了框架和多目标学习方法，其中回归任务是预测植物表型。