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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

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.

Authors who are presenting talks have a * after their name.

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