Fast Feature Selection with Fairness Constraints

TL;DR

This paper introduces a fast, parallel feature selection algorithm that incorporates fairness constraints, providing strong theoretical guarantees and competitive empirical performance on large datasets.

Contribution

It extends the adaptive query model to Orthogonal Matching Pursuit, enabling efficient, fair feature selection with proven approximation guarantees.

Findings

Achieves exponentially fast parallel runtime.

Incorporates fairness constraints via downward-closed sets.

Performs competitively against state-of-the-art methods.

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.