Feature selection for outcome prediction in oesophageal cancer using genetic algorithm and random forest classifier

2017 ◽  
Vol 60 ◽  
pp. 42-49 ◽  
Author(s):  
Desbordes Paul ◽  
Ruan Su ◽  
Modzelewski Romain ◽  
Vauclin Sébastien ◽  
Vera Pierre ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Random Forest ◽  
Oesophageal Cancer ◽  
Outcome Prediction ◽  
Random Forest Classifier ◽  
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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