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Scandent Tree: A Random Forest Learning Method for Incomplete Multimodal Datasets

  • Soheil Hor
  • Mehdi Moradi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9349)

Abstract

We propose a solution for training random forests on incomplete multimodal datasets where many of the samples are non-randomly missing a large portion of the most discriminative features. For this goal, we present the novel concept of scandent trees. These are trees trained on the features common to all samples that mimic the feature space division structure of a support decision tree trained on all features. We use the forest resulting from ensembling these trees as a classification model. We evaluate the performance of our method for different multimodal sample sizes and single modal feature set sizes using a publicly available clinical dataset of heart disease patients and a prostate cancer dataset with MRI and gene expression modalities. The results show that the area under ROC curve of the proposed method is less sensitive to the multimodal dataset sample size, and that it outperforms the imputation methods especially when the ratio of multimodal data to all available data is small.

Keywords

Random Forest Dynamic Contrast Enhance Multimodal Data Single Modality Tree Prostate Cancer Data 
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 Switzerland 2015

Authors and Affiliations

  • Soheil Hor
    • 1
  • Mehdi Moradi
    • 2
  1. 1.University of British ColumbiaVancouverCanada
  2. 2.IBM Almaden Research CenterSan JoseUSA

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