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的一种技术，用于识别记录中与目标变量具有强统计关联的特征。在训练中排除与目标变量统计关联较弱的特征，不仅降低了数据维度，从而减少了算法的时间复杂度，还降低了数据中的噪声，有助于避免过拟合。总之，特征选择有助于训练一个性能良好且稳定的鲁棒统计模型。鉴于经典计算缺乏可扩展性，当前技术仅考虑特征的预测能力，而未考虑特征之间的冗余性。最近利用量子退火（QA）的特征选择进展提供了一种可扩展的技术，旨在最大化特征的预测能力，同时最小化冗余性。因此，预期该算法将有助于偏差/方差权衡，从而为训练统计模型提供更好的特征。本文通过利用开源数据集测试了这一直觉与经典方法的对比，并使用众所周知的预测算法评估了每个训练好的统计模型的有效性。数值结果表明，利用基于QA算法选择的特征具有优势。