DocumentCode :
264277
Title :
Genetic programming as a feature selection algorithm
Author :
Suarez, Ranyart R. ; Valencia-Ramirez, Jose Maria ; Graff, Mario
fYear :
2014
fDate :
5-7 Nov. 2014
Firstpage :
1
Lastpage :
5
Abstract :
Genetic Programming (GP) is an Evolutionary Algorithm commonly used to evolve computer programs in order to solve a particular task. Therefore, GP has been used to tackle different problems like classification and regression. In this work, the capabilities of GP in other types of problems are explored, particularly the feature selection problem. For this purpose, GP is applied to a set of benchmark problems, and, then, compared to other algorithms. The results obtained show that GP is competitive against the other algorithms, and in addition to this, no modifications are needed to perform the feature extraction task.
Keywords :
feature extraction; feature selection; genetic algorithms; regression analysis; benchmark problems; computer programs; evolutionary algorithm; feature extraction task; feature selection algorithm; genetic programming; Accuracy; Algorithm design and analysis; Benchmark testing; Bit error rate; Feature extraction; Noise; Radio frequency;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Power, Electronics and Computing (ROPEC), 2014 IEEE International Autumn Meeting on
Conference_Location :
Ixtapa
Type :
conf
DOI :
10.1109/ROPEC.2014.7036345
Filename :
7036345
Link To Document :
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