Predicting Rice Phenotypes with Meta-learning

Abstract

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. We evaluated the frameworks on a genomic rice dataset where the regression task is to predict plant phenotype. We conclude that there are use cases for both frameworks.