Tree-Based Transforms for Privileged Learning

In many machine learning applications, samples are characterized by a variety of data modalities. In some instances, the training and testing data might include overlapping, but not identical sets of features. In this work, we describe a versatile decision forest methodology to train a classifier based on data that includes several modalities, and then deploy it for use with test data that only presents a subset of the modalities. To this end, we introduce the concept of cross-modality tree feature transforms. These are feature transformations that are guided by how a different feature partitions the training data. We have used the case of staging cognitive impairments to show the benefits of this approach. We train a random forest model that uses both MRI and PET, and can be tested on data that only includes MRI features. We show that the model provides an 8 % improvement in accuracy of separating of progressive cognitive impairments from stable impairments, compared to a model that uses MRI only for training and testing.