Quantum Feature Selection

• URL: http://arxiv.org/abs/2203.13261v1
• Date: Thu, 24 Mar 2022 16:22:25 GMT
• Title: Quantum Feature Selection
• Authors: Sascha M\"ucke, Raoul Heese, Sabine M\"uller, Moritz Wolter and Nico Piatkowski
• Abstract summary: In machine learning, fewer features reduce model complexity. We propose a novel feature selection algorithm based on a quadratic unconstrained binary optimization problem. In contrast to iterative or greedy methods, our direct approach yields higherquality solutions.
• Score: 2.5934039615414615
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In machine learning, fewer features reduce model complexity. Carefully assessing the influence of each input feature on the model quality is therefore a crucial preprocessing step. We propose a novel feature selection algorithm based on a quadratic unconstrained binary optimization (QUBO) problem, which allows to select a specified number of features based on their importance and redundancy. In contrast to iterative or greedy methods, our direct approach yields higherquality solutions. QUBO problems are particularly interesting because they can be solved on quantum hardware. To evaluate our proposed algorithm, we conduct a series of numerical experiments using a classical computer, a quantum gate computer and a quantum annealer. Our evaluation compares our method to a range of standard methods on various benchmark datasets. We observe competitive performance.

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