Tree-Based Transforms for Privileged Learning

  • Mehdi MoradiEmail author
  • Tanveer Syeda-Mahmood
  • Soheil Hor
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10019)


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.


Positron Emission Tomography Mild Cognitive Impairment Random Forest Support Tree Shared Modality 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Mehdi Moradi
    • 1
    Email author
  • Tanveer Syeda-Mahmood
    • 1
  • Soheil Hor
    • 2
  1. 1.IBM Almaden Research CenterSan JoseUSA
  2. 2.University of British ColumbiaVancouverCanada

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