ArticleSensors (Basel, Switzerland)2021
Machine Learning Methods for Fear Classification Based on Physiological Features.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- A logic-based knowledge-driven bidirectional multi-attention GRU framework for fear level classification in humans.Scientific reports · 2026Article
- Integrating MaxEnt and PLUS for Predictive Modeling of Mangrove Suitability and Land Use Impacts in Coastal Guangdong, China.Ecology and evolution · 2025Article
- Emotional salience network involved in constructing two-dimensional fear space in humans.Neurobiology of stress · 2025Review
- A Systematic Review of Species Classification Using Deep Learning Algorithms and Gender Identification ofZoological studies · 2025Article
- Strategies for Reliable Stress Recognition: A Machine Learning Approach Using Heart Rate Variability Features.Sensors (Basel, Switzerland) · 2024Article
- Effective relax acquisition: a novel approach to classify relaxed state in alpha band EEG-based transformation.Brain informatics · 2024Article
- Racial and ethnic differences in predictors of participation in an intergenerational social connectedness intervention for older adults.BMC geriatrics · 2024Article
- The role of machine learning methods in physiological explorations of endurance trained athletes: a mini-review.Frontiers in sports and active living · 2024Review
- Wearable Devices and Explainable Unsupervised Learning for COVID-19 Detection and Monitoring.Diagnostics (Basel, Switzerland) · 2023Article
- Quantitative identification of ventral/dorsal nerves through intraoperative neurophysiological monitoring by supervised machine learning.Frontiers in pediatrics · 2023Article
- Multi-Input CNN-LSTM deep learning model for fear level classification based on EEG and peripheral physiological signals.Frontiers in psychology · 2023Article
- Analysis of Marine-Pilot Biometric Data Recordings during Port-Approach Using a Full-Mission Simulator.Sensors (Basel, Switzerland) · 2022Article
- Evaluating Predictors of Participation in Telephone-Based Social-Connectedness Interventions for Older Adults: A Dual Machine-Learning and Regression Approach.Gerontology & geriatric medicineArticle
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
This paper focuses on the binary classification of the emotion of fear, based on the physiological data and subjective responses stored in the DEAP dataset. We performed a mapping between the discrete and dimensional emotional information considering the participants' ratings and extracted a substantial set of 40 types of features from the physiological data, which represented the input to various machine learning algorithms-Decision Trees, k-Nearest Neighbors, Support Vector Machine and artificial networks-accompanied by dimensionality reduction, feature selection and the tuning of the most relevant hyperparameters, boosting classification accuracy. The methodology we approached included tackling different situations, such as resolving the problem of having an imbalanced dataset through data augmentation, reducing overfitting, computing various metrics in order to obtain the most reliable classification scores and applying the Local Interpretable Model-Agnostic Explanations method for interpretation and for explaining predictions in a human-understandable manner. The results show that fear can be predicted very well (accuracies ranging from 91.7% using Gradient Boosting Trees to 93.5% using dimensionality reduction and Support Vector Machine) by extracting the most relevant features from the physiological data and by searching for the best parameters which maximize the machine learning algorithms' classification scores.
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