Data mining applied to feature selection methods for aboveground carbon stock modelling

Data mining applied to feature selection methods for aboveground carbon stock modelling

Abstract The objective of this work was to apply the random forest (RF) algorithm to the modelling of the aboveground carbon (AGC) stock of a tropical forest by testing three feature selection procedures – recursive removal and the uniobjective and multiobjective genetic algorithms (GAs). The used d...

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Bibliographic Details
Main Authors: Mônica Canaan Carvalho, Lucas Rezende Gomide, José Roberto Soares Scolforo, Kalill José Viana da Páscoa, Laís Almeida Araújo, Isáira Leite e Lopes
Format: Article
Language:English
Published: Embrapa Informação Tecnológica 2022-12-01
Series:Pesquisa Agropecuária Brasileira
Subjects:
forest management
genetic algorithm
random forest
Online Access:http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-204X2022000103800&tlng=en

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http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-204X2022000103800&tlng=en

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