PhD defense : Enhancing Supervised Learning with Complex Aggregate Features and Context Sensitivity

PhD defense : Clément CHARNAY

Team : SDC

Title : Enhancing Supervised Learning with Complex Aggregate Features and Context Sensitivity

Abstract : In this thesis, we study model adaptation in supervised learning. Firstly, we adapt existing learning algorithms to the relational representation of data. Secondly, we adapt learned prediction models to context change.
In the relational setting, data is modeled by multiples entities linked with relationships. We handle these relationships using complex aggregate features. We propose stochastic optimization heuristics to include complex aggregates in relational decision trees and Random Forests, and assess their predictive performance on real-world datasets.
We adapt prediction models to two kinds of context change. Firstly, we propose an algorithm to tune thresholds on pairwise scoring models to adapt to a change of misclassification costs. Secondly, we reframe numerical attributes with affine transformations to adapt to a change of attribute distribution between a learning and a deployment context. Finally, we extend these transformations to complex aggregates.

This thesis was supervised by Mr Nicolas Lachiche and co-diriged by Agnès Braud (ICube).

The presentation will take place on Thursday, June 30th 2016 at 2.00pm in room A301 of the pôle API building in Illkirch. This thesis will be supported in english.