Detecting Outliers in Terms of Errors in Embedded Software Development Projects Using Imbalanced Data Classification

Iwata, Kazunori; Nakashima, Toyoshiro; Anan, Yoshiyuki; Ishii, Naohiro

doi:10.1007/978-3-319-63618-4_6

Cited by 1 publication

(1 citation statement)

References 11 publications

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“…The detection of these outliers is crucial for understanding possible causes and implications of their presence (Cousineau and Chartier, 2010;Leys et al, 2013). Moreover, the importance of outliers has been studied in different domains and problems (Felt et al, 2017;Iwata et al, 2018;Peiffer and Armytage, 2019).…”

Section: Pre-processing: Dataset Analysismentioning

confidence: 99%

Framework for the Classification of Emotions in People With Visual Disabilities Through Brain Signals

López-Hernández

González‐Carrasco

López-Cuadrado

et al. 2021

Front. Neuroinform.

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Nowadays, the recognition of emotions in people with sensory disabilities still represents a challenge due to the difficulty of generalizing and modeling the set of brain signals. In recent years, the technology that has been used to study a person’s behavior and emotions based on brain signals is the brain–computer interface (BCI). Although previous works have already proposed the classification of emotions in people with sensory disabilities using machine learning techniques, a model of recognition of emotions in people with visual disabilities has not yet been evaluated. Consequently, in this work, the authors present a twofold framework focused on people with visual disabilities. Firstly, auditory stimuli have been used, and a component of acquisition and extraction of brain signals has been defined. Secondly, analysis techniques for the modeling of emotions have been developed, and machine learning models for the classification of emotions have been defined. Based on the results, the algorithm with the best performance in the validation is random forest (RF), with an accuracy of 85 and 88% in the classification for negative and positive emotions, respectively. According to the results, the framework is able to classify positive and negative emotions, but the experimentation performed also shows that the framework performance depends on the number of features in the dataset and the quality of the Electroencephalogram (EEG) signals is a determining factor.

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