An Advantage Using Feature Selection with a Quantum Annealer
This work addresses scalability issues in feature selection for machine learning practitioners, offering a quantum-based method that improves model performance, though it appears incremental as it builds on existing quantum annealing advancements.
The paper tackled the problem of feature selection in statistical prediction modeling by leveraging quantum annealing to maximize predictive power while minimizing redundancy, and found that this approach provided an advantage over classical methods in numerical tests on open-source datasets.
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.