Name
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. 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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s10994-020-05881-9 | MACHINE LEARNING |
Keywords | DocType | Volume |
Rice, Bioinformatics, Machine learning, Meta-learning, Multi-target learning | Journal | 109 |
Issue | ISSN | Citations |
11 | 0885-6125 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Oghenejokpeme I. Orhobor | 1 | 3 | 1.72 |
| Nickolai N Alexandrov | 2 | 6 | 2.29 |
| Ross D. King | 3 | 1774 | 194.85 |