Wrapper for Building Classification Models Using Covering Arrays

Wrapper for Building Classification Models Using Covering Arrays

Wrapper methods are a type of feature selection method that finds a subset of variables to improve the performance of a classifier by removing redundant and irrelevant variables. The use of a wrapper implies that each time a candidate solution is explored, the classifier is evaluated on the quality...

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Bibliographic Details
Main Authors: Hugo Dorado, Carlos Cobos, Jose Torres-Jimenez, Dharani Dhar Burra, Martha Mendoza, Daniel Jimenez
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Classification algorithms
covering arrays
random forest
support vector machines
genetic algorithms
particle swarm optimization
Online Access:https://ieeexplore.ieee.org/document/8853323/

Internet

https://ieeexplore.ieee.org/document/8853323/

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