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ST-CIRL: a reinforcement learning-based feature selection approach for enhanced anxiety classification.

Shikha Shikha¹, Divyashikha Sethia², S Indu³

¹Computer Science and engineering, Delhi Technological University, Shahbad Daulatpur, Main Bawana Road, Delhi-110042, New Delhi, New Delhi, Delhi, 110042, INDIA.

Physiological Measurement

|January 29, 2025

Summary

This summary is machine-generated.

This study introduces a novel framework for anxiety classification using physiological signals, achieving high accuracy through advanced feature selection and reinforcement learning. The method enhances human-computer interaction (HCI) systems by improving emotional state recognition.

Keywords:

Feature selection Imbalanced data Machine Learning Optuna Reinforcement Learning physiological signals

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Area of Science:

Physiological signal processing
Human-Computer Interaction (HCI)
Machine Learning for Affective Computing

Background:

Effective Human-Computer Interaction (HCI) relies on accurately interpreting human emotional states from physiological signals.
Classifying these signals requires robust feature extraction and selection to differentiate emotions.
Existing methods face challenges with class imbalance and feature redundancy.

Purpose of the Study:

To introduce the SMOTETomek-Correlated Interactive Reinforcement Learning (ST-CIRL) framework for enhanced anxiety classification.
To leverage meta-descriptive statistics for improved state representation in reinforcement learning.
To optimize feature selection and classification performance in HCI systems.

Main Methods:

Addressing class imbalance with SMOTETomek and reducing dimensionality by pruning redundant features.
Employing Interactive Reinforcement Learning (IRL) with multi-agent collaboration for informative feature selection.
Utilizing and tuning classifiers (Random Forest, SVM, KNN, LightGBM) with the Optuna approach.

Main Results:

The ST-CIRL framework achieved a maximum accuracy of 95.35% and an F1-score of 95.49% using the LightGBM classifier.
The proposed approach demonstrated superior performance compared to current state-of-the-art methods.
Validation of SMOTETomek for imbalance handling and feature optimization's effectiveness.

Conclusions:

The ST-CIRL framework significantly enhances anxiety classification accuracy in HCI systems.
Reinforcement learning shows strong potential for improving physiological signal-based HCI.
The developed feature optimization strategy is effective for intelligent system design.