# An Advantage Using Feature Selection with a Quantum Annealer

arXiv: https://arxiv.org/abs/2211.09756 · HTML: https://mlsanity.com/paper/2211.09756/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 37 references that are in the map.
- First posted on arXiv 17 Nov 2022.

## Builds on (most cited first, 6 of 6)

- [Feature Selection on Quantum Computers](https://mlsanity.com/paper/2203.13261/) (2022)
- [Maximum Relevance and Minimum Redundancy Feature Selection Methods for a Marketing Machine Learning Platform](https://mlsanity.com/paper/1908.05376/) (2019)
- [Quantum Annealing Formulation for Binary Neural Networks](https://mlsanity.com/paper/2107.02751/) (2021)
- [Feature Selection for Recommender Systems with Quantum Computing](https://mlsanity.com/paper/2110.05089/) (2021)
- [Review of Swarm Intelligence-based Feature Selection Methods](https://mlsanity.com/paper/2008.04103/) (2020)
- [Quantum-Assisted Feature Selection for Vehicle Price Prediction Modeling](https://mlsanity.com/paper/2104.04049/) (2021)

## Built on by (most cited first, 1 of 1)

- [Investigation of factors regarding the effects of COVID-19 pandemic on college students' depression by quantum annealer](https://mlsanity.com/paper/2310.00018/) (2023)

## Abstract (arXiv)

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. Given the lack of scalability in classical computation, current techniques only consider the predictive power of the feature and not redundancy between the features themselves. Recent advancements in feature selection that leverages quantum annealing (QA) gives 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 evaluate 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.