Learning Interpretable Cascade Classifiers (Applications in Medicine) - Nataliya Sokolovska, Sorbonne University


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In many prediction tasks such as medical diagnostics, sequential decisions are crucial to provide optimal individual treatment. Budget in real-life applications is always limited, and it can represent any limited resource such as time, money, or side effects of medications. The goal of cascade classifiers under budget constraints is to classify examples with low cost, and to minimise the number of expensive or time-consuming features (or measurements). I will present an approach to learn cost-sensitive heterogeneous cascading systems.



Nataliya Sokolovska did her PhD in Machine Learning (feature selection and semi-supervised learning) at Telecom ParisTech in 2010. She was post-doctoral researcher at the LRI (Laboratoire de recherche en informatique) at University Paris XI (Orsay) in 2010 - 2011 and at the Department of Computing, Macquarie University, Sydney, Australia in 2011 - 2012. From 2012 Nataliya is an assistant professor (maitre de conférences) in Sorbonne University (University Paris 6) working on machine learning with applications in medicine and fundamental biology.