ensemble-integration: Integrating multi-modal data for predictive modeling
ensemble-integration (or eipy) leverages multi-modal data to build classifiers using a late fusion approach.
In eipy, base predictors are trained on each modality before being ensembled at the late stage.
This implementation of eipy can utilize sklearn-like models only, therefore, for unstructured data,
e.g. images, it is recommended to perform feature selection prior to using eipy. We hope to allow for a wider range of base predictors,
i.e. deep learning methods, in future releases. A key feature of eipy is its built-in nested cross-validation approach, allowing for a
fair comparison of a collection of user-defined ensemble methods.
For more details see the original publication.