DMS Statistics and Data Science Seminar

Speaker: Roberto Molinari (Auburn University)

Title: SWAG: A Sparse Wrapper Algorithm with Applications in Gene Selection

Abstract: In genomics an important goal is to achieve high predictive (classification) power using as few gene expressions as possible to facilitate replicability and interpretability for research and diagnostic purposes. Given these goals, the reliance on a single model/learner, possibly issued from a sparse learning method, very probably doesn’t respond to these needs in many cases. We put forward a heuristic greedy algorithm that allows to find a set/library of extremely sparse and highly predictive learners and discuss how this procedure could be modified to select learners based on other (non-convex) utility functions.