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