Classification of electroencephalogram signal for the detection of epilepsy using Innovative Genetic Programming

2018 ◽  
Vol 36 (1) ◽  
pp. e12338 ◽  
Author(s):  
Harshit Bhardwaj ◽  
Aditi Sakalle ◽  
Arpit Bhardwaj ◽  
Aruna Tiwari
Keyword(s):  
Genetic Programming

Classification of EEG Signals using Genetic Programming

2017 ◽  
Author(s):  
Marco Antonio Meggiolaro ◽  
Felipe Rebelo Lopes
Keyword(s):  
Genetic Programming ◽  
Eeg Signals

scholarly journals Improving Land Cover Classification Using Genetic Programming for Feature Construction

2021 ◽  
Vol 13 (9) ◽  
pp. 1623
Author(s):  
João E. Batista ◽  
Ana I. R. Cabral ◽  
Maria J. P. Vasconcelos ◽  
Leonardo Vanneschi ◽  
Sara Silva
Keyword(s):  
Land Cover ◽  
Genetic Programming ◽  
Satellite Images ◽  
State Of The Art ◽  
Binary Classification ◽  
Feature Construction ◽  
Classification Problems ◽  
Construction Methods ◽  
Box Models

Genetic programming (GP) is a powerful machine learning (ML) algorithm that can produce readable white-box models. Although successfully used for solving an array of problems in different scientific areas, GP is still not well known in the field of remote sensing. The M3GP algorithm, a variant of the standard GP algorithm, performs feature construction by evolving hyperfeatures from the original ones. In this work, we use the M3GP algorithm on several sets of satellite images over different countries to create hyperfeatures from satellite bands to improve the classification of land cover types. We add the evolved hyperfeatures to the reference datasets and observe a significant improvement of the performance of three state-of-the-art ML algorithms (decision trees, random forests, and XGBoost) on multiclass classifications and no significant effect on the binary classifications. We show that adding the M3GP hyperfeatures to the reference datasets brings better results than adding the well-known spectral indices NDVI, NDWI, and NBR. We also compare the performance of the M3GP hyperfeatures in the binary classification problems with those created by other feature construction methods such as FFX and EFS.

An Improved Genetic Programming Technique for the Classification of Raman Spectra

2007 ◽  
pp. 181-192
Author(s):  
Kenneth Hennessy ◽  
Michael G. Madden ◽  
Jennifer Conroy ◽  
Alan G. Ryder
Keyword(s):  
Genetic Programming ◽  
Raman Spectra ◽  
Programming Technique
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