Abstract
We describe a method for classifying subjects based on functional magnetic-resonance (fMR) data, using a method combining a Bayesian-network classifier with inverse-tree structure (BNCIT), and ensemble learning. The central challenge is to generate a classifier from a small sample of high-dimensional data. The principal strengths of our method include the nonparametric multivariate Bayesian-network representation, and joint performance of feature selection and classification. Preliminary results indicate that this method can detect regions characterizing group differences, and can, on the basis of activation levels in these regions, accurately classify new subjects.
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Acknowledgements
This work was supported by The Human Brain Project, National Institutes of Health grant R01 AG13743, which is funded by the National Institute of Aging, the National Institute of Mental Health, and the National Cancer Institute.
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Chen, R., Herskovits, E.H. Clinical Diagnosis Based on Bayesian Classification of Functional Magnetic-Resonance Data. Neuroinform 5, 178–188 (2007). https://doi.org/10.1007/s12021-007-0007-2
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DOI: https://doi.org/10.1007/s12021-007-0007-2