Quantum Annealing for Automated Feature Selection in Stress Detection

TL;DR

This paper explores using Quantum Annealing to automate feature selection from physiological signals for stress detection, demonstrating robustness under limited data conditions and potential for optimizing machine learning training.

Contribution

It introduces a QA-based feature selection method embedded in a binary quadratic model for stress detection from physiological signals, showing robustness under data uncertainty.

Findings

QA-based feature selection performs comparably to classical methods.

Under limited data, QA maintains performance while classical methods decline.

Preliminary results suggest QA can optimize machine learning training phases.

Abstract

We present a novel methodology for automated feature subset selection from a pool of physiological signals using Quantum Annealing (QA). As a case study, we will investigate the effectiveness of QA-based feature selection techniques in selecting the optimal feature subset for stress detection. Features are extracted from four signal sources: foot EDA, hand EDA, ECG, and respiration. The proposed method embeds the feature variables extracted from the physiological signals in a binary quadratic model. The bias of the feature variable is calculated using the Pearson correlation coefficient between the feature variable and the target variable. The weight of the edge connecting the two feature variables is calculated using the Pearson correlation coefficient between two feature variables in the binary quadratic model. Subsequently, D-Wave's clique sampler is used to sample cliques from the binary quadratic model. The underlying solution is then re-sampled to obtain multiple good solutions and the clique with the lowest energy is returned as the optimal solution. The proposed method is compared with commonly used feature selection techniques for stress detection. Results indicate that QA-based feature subset selection performed equally as that of classical techniques. However, under data uncertainty conditions such as limited training data, the performance of quantum annealing for selecting optimum features remained unaffected, whereas a significant decrease in performance is observed with classical feature selection techniques. Preliminary results show the promise of quantum annealing in optimizing the training phase of a machine learning classifier, especially under data uncertainty conditions.