Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Cao, Y., Wang, H., Moradi, M., Prasanna, P., Syeda-Mahmood, T.F.: Fracture detection in X-ray images through stacked random forests feature fusion. In: IEEE ISBI, pp. 801–805 (2015)
Hor, S., Moradi, M.: Scandent tree: a random forest learning method for incomplete multimodal datasets. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9349, pp. 694–701. Springer, Heidelberg (2015). doi:10.1007/978-3-319-24553-9_85
Steinberg, D., Colla, P.: Cart: classification and regression trees. Top Ten Algorithms Data Min. 9, 179 (2009)
Therneau, T.M., Atkinson, B., Ripley, B.: RPART: recursive partitioning. R package version 3.1-46. Ported to R by Brian Ripley. 3 (2010)
Vapnik, V., Izmailov, R.: Learning using privileged information: similarity control and knowledge transfer. J. Mach. Learn. Res. 16, 2023–2049 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Moradi, M., Syeda-Mahmood, T., Hor, S. (2016). Tree-Based Transforms for Privileged Learning. In: Wang, L., Adeli, E., Wang, Q., Shi, Y., Suk, HI. (eds) Machine Learning in Medical Imaging. MLMI 2016. Lecture Notes in Computer Science(), vol 10019. Springer, Cham. https://doi.org/10.1007/978-3-319-47157-0_23
Download citation
DOI: https://doi.org/10.1007/978-3-319-47157-0_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-47156-3
Online ISBN: 978-3-319-47157-0
eBook Packages: Computer ScienceComputer Science (R0)