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Composite Feature Selection Using Deep Ensembles
Fergus Imrie · Alexander Norcliffe · Pietro Lió · Mihaela van der Schaar

Tue Nov 29 02:00 PM -- 04:00 PM (PST) @ Hall J #229

In many real world problems, features do not act alone but in combination with each other. For example, in genomics, diseases might not be caused by any single mutation but require the presence of multiple mutations. Prior work on feature selection either seeks to identify individual features or can only determine relevant groups from a predefined set. We investigate the problem of discovering groups of predictive features without predefined grouping. To do so, we define predictive groups in terms of linear and non-linear interactions between features. We introduce a novel deep learning architecture that uses an ensemble of feature selection models to find predictive groups, without requiring candidate groups to be provided. The selected groups are sparse and exhibit minimum overlap. Furthermore, we propose a new metric to measure similarity between discovered groups and the ground truth. We demonstrate the utility our model on multiple synthetic tasks and semi-synthetic chemistry datasets, where the ground truth structure is known, as well as an image dataset and a real-world cancer dataset.

Author Information

Fergus Imrie (University of California, Los Angeles)
Alexander Norcliffe (University of Cambridge)

I'm a PhD student in Machine Learning for Medicine. I am co-supervised by Mihaela Van der Schaar and Pietro Lio in the Cambridge Centre for AI in Medicine.

Pietro Lió (University of Cambridge)
Mihaela van der Schaar (University of Cambridge)

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