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22.03.2021
Martin Pagel

Paper “Enforcing Constraints for Machine Learning Systems via Declarative Feature Selection: An Experimental Study” Accepted for Presentation at SIGMOD 2021

The research paper “Enforcing Constraints for Machine Learning Systems via Declarative Feature Selection: An Experimental Study,” authored by Felix Neutatz, Felix Bießmann, Ziawasch Abedjan was accepted for presentation at the ACM SIGMOD International Conference on Management of Data (SIGMOD/PODS 2021), June 20-25, 2021, Xi'an, Shaanxi, China.

Abstract:
Responsible usage of Machine Learning (ML) systems in practice does not only require enforcing high prediction quality, but also accounting for other constraints, such as fairness, privacy, or execution time. One way to address multiple user-specified constraints on ML systems is feature selection. Yet, optimizing feature selection strategies for multiple metrics is difficult to implement and has been underrepresented in previous experimental studies. Here, we propose Declarative Feature Selection (DFS) to simplify the de-sign and validation of ML systems satisfying diverse user-specified constraints. We benchmark and evaluate a representative series of feature selection algorithms. From our extensive experimental results, we derive concrete suggestions on when to use which strategy and show that a meta-learning-driven optimizer can accurately predict the right strategy for an ML task at hand. These results demonstrate that feature selection can help to build ML systems that meet combinations of user-specified constraints, independent of the ML methods used.

> Preprint "Enforcing Constraints for Machine Learning Systems via Declarative Feature Selection: An Experimental Study" (PDF)

To learn more about SIGMOD/PODS, please visit https://2021.sigmod.org/.