A novel fitness function in genetic programming to handle unbalanced emotion recognition data

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@Article{ACHARYA:2020:PRL,

• author = "Divya Acharya and Shivani Goel and Rishi Asthana and Arpit Bhardwaj",
• title = "A novel fitness function in genetic programming to handle unbalanced emotion recognition data",
• journal = "Pattern Recognition Letters",
• volume = "133",
• pages = "272--279",
• year = "2020",
• ISSN = "0167-8655",
• DOI = "doi:10.1016/j.patrec.2020.03.005",
• URL = "http://www.sciencedirect.com/science/article/pii/S0167865520300830",
• keywords = "genetic algorithms, genetic programming, Emotion recognition, Fitness function, EEG, Fast Fourier transformation",
• abstract = "In the area of behavioral psychology, real-time emotion recognition by using physiological stimuli is an active topic of interest. This research considers the recognition of two class of emotions i.e., positive and negative emotions using EEG signals in response to happy, horror, sad, and neutral genres. In a noise-free framework for data acquisition of 50 participants, NeuroSky MindWave 2 is used. The dataset collected is unbalanced i.e., there are more instances of positive classes than negative ones. Therefore, accuracy is not a useful metric to assess the results of the unbalanced dataset because of biased results. So, the primary goal of this research is to address the issue of unbalanced emotion recognition dataset classification, for which we are proposing a novel fitness function known as Gap score (G score), which learns about both the classes by giving them equal importance and being unbiased. The genetic programming (GP) framework in which we implemented G score is named as G-score GP (GGP). The second goal is to assess how distinct genres affect human emotion recognition process and to identify an age group that is more active emotionally when their emotions are elicited. Experiments were conducted on EEG data acquired with a single-channel EEG device. We have compared the performance of GGP for the classification of emotions with state-of-the-art methods. The analysis shows that GGP provides 87.61percent classification accuracy by using EEG. In compliance with the self-reported feelings, brain signals of 26 to 35 years of age group provided the highest emotion recognition rate",

}

Genetic Programming entries for Divya Acharya Shivani Goel Rishi Asthana Arpit Bhardwaj

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