An enhanced fitness function to recognize unbalanced human emotions data
Created by W.Langdon from
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- @Article{ACHARYA:2021:ESA,
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author = "Divya Acharya and Nandana Varshney and
Anindiya Vedant and Yashraj Saxena and Pradeep Tomar and
Shivani Goel and Arpit Bhardwaj",
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title = "An enhanced fitness function to recognize unbalanced
human emotions data",
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journal = "Expert Systems with Applications",
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volume = "166",
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pages = "114011",
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year = "2021",
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ISSN = "0957-4174",
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DOI = "doi:10.1016/j.eswa.2020.114011",
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URL = "https://www.sciencedirect.com/science/article/pii/S0957417420307843",
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keywords = "genetic algorithms, genetic programming, Emotion
recognition, Fitness function, EEG, Fast Fourier
Transformation, Unbalanced dataset",
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abstract = "In cognitive science and human-computer interaction,
automatic human emotion recognition using physiological
stimuli is a key technology. This research considers
two-class (positive and negative) of emotions
recognition using electroencephalogram (EEG) signals in
response to an emotional clip from the genres happy,
amusement, sad, and horror. This paper introduces an
enhanced fitness function named as eD-score to
recognize emotions using EEG signals. The primary goal
of this research is to assess how genres affect human
emotions. We also analyzed human behaviour based on age
and gender responsiveness. We have compared the
performance of Multilayer Perceptron (MLP), K-nearest
neighbors (KNN), Support Vector Machine (SVM), D-score
Genetic Programming (DGP), and enhanced D-score Genetic
Programming (eDGP) for classification of emotions. The
analysis shows that for two class of emotion eDGP
provides classification accuracy as 83.33percent,
84.69percent, 85.88percent, and 87.61percent for 50-50,
60-40, 70-30, and 10-fold cross-validations.
Generalizability and reliability of this approach is
evaluated by applying the proposed approach to publicly
available EEG datasets DEAP and SEED. When participants
in this research are exposed to amusement genre, their
reaction is positive emotion. In compliance with the
self-reported feelings, brain signals of 26-35 years of
age group provided the highest emotional
identification. Among genders, females are more
emotionally active as compared to males. These results
affirmed the potential use of our method for
recognizing emotions",
- }
Genetic Programming entries for
Divya Acharya
Nandana Varshney
Anindiya Vedant
Yashraj Saxena
Pradeep Tomar
Shivani Goel
Arpit Bhardwaj
Citations