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.

翻译：特征选择是统计预测建模中识别与目标变量具有强统计关联的记录特征的技术。在训练中剔除与目标变量统计关联较弱的特征，不仅能通过降低数据维度减少算法的时间复杂度，还能降低数据噪声，从而有助于避免过拟合。总体而言，特征选择有助于训练性能优异且稳定的鲁棒统计模型。鉴于经典计算在可扩展性方面的局限性，现有技术仅考虑特征的预测能力，而未考虑特征之间的冗余性。近期利用量子退火（QA）技术的特征选择方法提供了一种可扩展的解决方案，旨在最大化特征预测能力的同时最小化冗余性。因此，预期该算法有助于平衡偏差/方差权衡，从而为训练统计模型提供更优特征。本文通过利用开源数据集，并采用经典方法对基于QA算法所选特征训练的统计模型进行检验，评估了各已知预测算法的有效性。数值结果表明，利用基于量子退火的算法所选特征展现出显著优势。