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Activity Number: 50 - Machine Learning and Statistical Inference: Building Breiman's Bridge
Type: Invited
Date/Time: Sunday, July 30, 2017 : 4:00 PM to 5:50 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #322124
Title: Detecting Local Sparsity and High-Order Interactions with Iterative Random Forests
Author(s): Sumanta Basu* and James Bentley Brown and Bin Yu
Companies: Cornell University and Lawrence Berkeley National Laboratory and University of California, Berkeley
Keywords: random forests ; epigenetics ; feature selection ; sparsity ; high-order interaction
Abstract:

Identification of high-order epigenetic interactions among large biomolecules from next generation sequencing datasets (NGS) poses considerable challenges due to high-dimensionality of feature space and heterogeneity of human genome. Through extensive and realistic simulations, we have developed a method to detect biologically meaningful local, high-order interactions from these datasets in a stable fashion. Our method, iterative Random Forests (iRF), iteratively grows a sequence of feature weighted Random Forests, and searches for high-order interactions by analyzing feature usage on the decision paths of large, pure leaf nodes in the tree ensemble. In this work, we study the properties of iRF on a biologically inspired novel class of locally sparse, nonlinear and non-smooth models. We analyze both prediction and feature selection properties of iRF and propose principled guidelines to assess estimation stability of the selected features and their interactions.


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