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
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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