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Feature selection through quantum annealing

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Abstract

Feature selection is a technique in statistical prediction modeling that identifies features in a record with a strong statistical connection to the target variable. Excluding features with a weak statistical connection to the target variable in training not only drops the dimension of the data, which decreases the time complexity of the algorithm, it also decreases noise within the data which assists in avoiding overfitting. In all, feature selection assists in training a robust statistical model that performs well and is stable. Recent advancements in feature selection that leverages quantum annealing (QA) give a scalable technique that aims to maximize the predictive power of the features while minimizing redundancy. As a consequence, it is expected that this algorithm would assist in the bias/variance trade-off yielding better features for training a statistical model. This paper tests this intuition against classical methods by utilizing open-source data sets and evaluates the efficacy of each trained statistical model well-known prediction algorithms. The numerical results display an advantage utilizing the features selected from the algorithm that leveraged QA.

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Data availability

No datasets were generated or analysed during the current study.

Notes

  1. https://www.kaggle.com/datasets/arbazkhan971/anomaly-detection.

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Authors and Affiliations

  1. Deloitte Consulting, LLP, Chicago, United States

    Andrew Vlasic, Hunter Grant & Salvatore Certo

Authors
  1. Andrew Vlasic
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  2. Hunter Grant
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  3. Salvatore Certo
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Contributions

A.V., H.G., and S.C all equally contributed to the experiments, A.V. and S.C. wrote the main manuscript text, and A.V. prepared figures 1-3. All authors reviewed the manuscript.

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Correspondence to Andrew Vlasic.

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Vlasic, A., Grant, H. & Certo, S. Feature selection through quantum annealing. J Supercomput 81, 147 (2025). https://doi.org/10.1007/s11227-024-06673-x

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