Genetic Algorithm Based Feature Selection for Software Reliability Prediction Using Multi-layer Perceptron

2021 ◽  
pp. 436-446
Author(s):  
Priyanka Kumari ◽  
Kulamala Vinod Kumar ◽  
Durga Prasad Mohapatra
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Software Reliability ◽  
Reliability Prediction ◽  
Multi Layer Perceptron ◽  
Software Reliability Prediction ◽  
Selection For

Feature selection for classification in Steady state visually evoked potentials (SSVEP)-based brain-computer interfaces with genetic algorithm

2020 ◽  
Vol 16 (2) ◽  
Author(s):  
Stanisław Karkosz ◽  
Marcin Jukiewicz
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Steady State ◽  
Brain Computer Interface ◽  
Morphological Features ◽  
Computer Interface ◽  
Visually Evoked Potentials ◽  
Computer Interfaces ◽  
Subject Variability ◽  
Selection For

AbstractObjectivesOptimization of Brain-Computer Interface by detecting the minimal number of morphological features of signal that maximize accuracy.MethodsSystem of signal processing and morphological features extractor was designed, then the genetic algorithm was used to select such characteristics that maximize the accuracy of the signal’s frequency recognition in offline Brain-Computer Interface (BCI).ResultsThe designed system provides higher accuracy results than a previously developed system that uses the same preprocessing methods, however, different results were achieved for various subjects.ConclusionsIt is possible to enhance the previously developed BCI by combining it with morphological features extraction, however, it’s performance is dependent on subject variability.

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