Abstract
Ensembling methods are well known for improving prediction accuracy. However, they are limited
in the sense that they cannot effectively discriminate among component models. In this paper, we
propose stacking with auxiliary features that learns
to fuse additional relevant information from multiple component systems as well as input instances
to improve performance. We use two types of auxiliary features – instance features and provenance
features. The instance features enable the stacker to
discriminate across input instances and the provenance features enable the stacker to discriminate
across component systems. When combined together, our algorithm learns to rely on systems that
not just agree on an output but also the provenance
of this output in conjunction with the properties of
the input instance. We demonstrate the success of
our approach on three very different and challenging natural language and vision problems: Slot Filling, Entity Discovery and Linking, and ImageNet
Object Detection. We obtain new state-of-the-art
results on the first two tasks and significant improvements on the ImageNet task, thus verifying
the power and generality of our approach