TY - GEN

T1 - Enforcing Constraints for Machine Learning Systems via Declarative Feature Selection

T2 - An Experimental Study.

AU - Neutatz, Felix

AU - Biessmann, Felix

AU - Abedjan, Ziawasch

PY - 2021/6/9

Y1 - 2021/6/9

N2 - 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 design 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.

AB - 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 design 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.

KW - DFS

KW - bias

KW - declarative feature selection

KW - declarative machine learning

KW - declarative ml

KW - fairness

KW - feature selection

KW - machine learning

KW - meta learning

KW - privacy

KW - robustness

UR - http://www.scopus.com/inward/record.url?scp=85108980866&partnerID=8YFLogxK

U2 - 10.1145/3448016.3457295

DO - 10.1145/3448016.3457295

M3 - Conference contribution

T3 - Proceedings of the ACM SIGMOD International Conference on Management of Data

SP - 1345

EP - 1358

BT - SIGMOD/PODS '21: Proceedings of the 2021 International Conference on Management of Data

ER -