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.
Chapter PDF
Similar content being viewed by others
Keywords
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.
References
Liu, J., Calhoun, V.D.: A review of multivariate analyses in imaging genetics. Frontiers in Neuroinformatics 8, 29 (2014)
Rubin, D.B.: Multiple imputation for nonresponse in surveys, vol. 81. John Wiley & Sons (2004)
Gold, M.S., Bentler, P.M.: Treatments of missing data: A Monte Carlo comparison of RBHDI, iterative stochastic regression imputation, and expectation-maximization. Structural Equation Modeling 7(3), 319–355 (2000)
Kong, A., Liu, J.S., Wong, W.H.: Sequential imputations and bayesian missing data problems. Journal of the American Statistical Association 89(425), 278–288 (1994)
Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)
Liaw, A., Wiener, M.: Classification and regression by randomforest. R News 2(3), 18–22 (2002)
Therneau, T.M., Atkinson, B., Ripley, B.: rpart: Recursive partitioning. R package version 3.1-46. Ported to R by Brian Ripley 3 (2010)
Lichman, M.: UCI machine learning repository (2013)
Detrano, R., Janosi, A., Steinbrunn, W., Pfisterer, M., Schmid, J.J., Sandhu, S., Guppy, K.H., Lee, S., Froelicher, V.: International application of a new probability algorithm for the diagnosis of coronary artery disease. The American Journal of Cardiology 64(5), 304–310 (1989)
Haq, N.F., Kozlowski, P., Jones, E.C., Chang, S.D., Goldenberg, S.L., Moradi, M.: A data-driven approach to prostate cancer detection from dynamic contrast enhanced MRI. Computerized Medical Imaging and Graphics 41, 37–45 (2015)
Moradi, M., Salcudean, S.E., Chang, S.D., Jones, E.C., Buchan, N., Casey, R.G., Goldenberg, S.L., Kozlowski, P.: Multiparametric MRI maps for detection and grading of dominant prostate tumors. Journal of Magnetic Resonance Imaging 35(6), 1403–1413 (2012)
Erho, N., et al.: Discovery and validation of a prostate cancer genomic classifier that predicts early metastasis following radical prostatectomy. PloS One 8(6), e66855 (2013)
National Institutes of Health: National cancer institute: PDQ genetics of prostate cancer (Date last modified February 20, 2015)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Hor, S., Moradi, M. (2015). Scandent Tree: A Random Forest Learning Method for Incomplete Multimodal Datasets. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds) Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science(), vol 9349. Springer, Cham. https://doi.org/10.1007/978-3-319-24553-9_85
Download citation
DOI: https://doi.org/10.1007/978-3-319-24553-9_85
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-24552-2
Online ISBN: 978-3-319-24553-9
eBook Packages: Computer ScienceComputer Science (R0)