Machine Learning Methods for Fear Classification Based on Physiological Features
Autor: | Ana Oprea, Gabriela Moise, Alin Moldoveanu, Catalin Petrescu, Oana Mitruț, Florica Moldoveanu, Livia Petrescu |
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Jazyk: | angličtina |
Rok vydání: | 2021 |
Předmět: |
Boosting (machine learning)
Support Vector Machine Computer science Feature selection 02 engineering and technology TP1-1185 Overfitting Machine learning computer.software_genre 01 natural sciences Biochemistry Article Analytical Chemistry emotion dimensions 0202 electrical engineering electronic engineering information engineering Cluster Analysis Humans Electrical and Electronic Engineering Instrumentation Artificial neural network business.industry Dimensionality reduction Chemical technology 010401 analytical chemistry Fear neural networks Atomic and Molecular Physics and Optics 0104 chemical sciences emotion classification Support vector machine machine learning Binary classification fear classification 020201 artificial intelligence & image processing Artificial intelligence Gradient boosting business computer Algorithms |
Zdroj: | Sensors Volume 21 Issue 13 Sensors, Vol 21, Iss 4519, p 4519 (2021) Sensors (Basel, Switzerland) |
ISSN: | 1424-8220 |
DOI: | 10.3390/s21134519 |
Popis: | 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. |
Databáze: | OpenAIRE |
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