Fast Feature Selection with Fairness Constraints
- URL: http://arxiv.org/abs/2202.13718v2
- Date: Fri, 3 Feb 2023 13:03:43 GMT
- Title: Fast Feature Selection with Fairness Constraints
- Authors: Francesco Quinzan, Rajiv Khanna, Moshik Hershcovitch, Sarel Cohen,
Daniel G. Waddington, Tobias Friedrich, Michael W. Mahoney
- Abstract summary: We study the fundamental problem of selecting optimal features for model construction.
This problem is computationally challenging on large datasets, even with the use of greedy algorithm variants.
We extend the adaptive query model, recently proposed for the greedy forward selection for submodular functions, to the faster paradigm of Orthogonal Matching Pursuit for non-submodular functions.
The proposed algorithm achieves exponentially fast parallel run time in the adaptive query model, scaling much better than prior work.
- Score: 49.142308856826396
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: We study the fundamental problem of selecting optimal features for model
construction. This problem is computationally challenging on large datasets,
even with the use of greedy algorithm variants. To address this challenge, we
extend the adaptive query model, recently proposed for the greedy forward
selection for submodular functions, to the faster paradigm of Orthogonal
Matching Pursuit for non-submodular functions. The proposed algorithm achieves
exponentially fast parallel run time in the adaptive query model, scaling much
better than prior work. Furthermore, our extension allows the use of
downward-closed constraints, which can be used to encode certain fairness
criteria into the feature selection process. We prove strong approximation
guarantees for the algorithm based on standard assumptions. These guarantees
are applicable to many parametric models, including Generalized Linear Models.
Finally, we demonstrate empirically that the proposed algorithm competes
favorably with state-of-the-art techniques for feature selection, on real-world
and synthetic datasets.
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