Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp01cz30pw02g
 Title: Imbalanced fMRI Classification: An Ensemble Approach Authors: Nathan, Shreya Advisors: Liu, Han Department: Operations Research and Financial Engineering Class Year: 2015 Abstract: Functional Magnetic Resonance Imaging (fMRI) has recently come to dominate the brain mapping field due to its reliable, noninvasive ability to gather massive amounts of data about task-specific brain activity. The high-dimensional, often noisy datasets recorded by these studies, however, pose significant statistical challenges that must be overcome before the data is ready for neurological interpretation. One such challenge is that of imbalanced data, where instances of one class far outnumber the other, which makes accurate classification very difficult by traditional methods. This thesis studies a series of imbalanced datasets from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and develops a new three-phase ensemble model that combines majority class undersampling through unsupervised K-Medoids clustering, an iterative l$$_{2}$$ logistic loss algorithm for feature selection, and a divide and conquer classification strategy to produce reliably stronger performance than the field’s current frontrunners. Extent: 57 pages URI: http://arks.princeton.edu/ark:/88435/dsp01cz30pw02g Type of Material: Princeton University Senior Theses Language: en_US Appears in Collections: Operations Research and Financial Engineering, 2000-2017