Variable selection for estimating individual tree height using genetic algorithm and random forest-Reference-Cited by-同舟云学术

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Variable selection for estimating individual tree height using genetic algorithm and random forest

Published:2022-01 Issue: Volume:504 Page:119828
ISSN:0378-1127
Container-title:Forest Ecology and Management
language:en
Short-container-title:Forest Ecology and Management

Author:

Nunes Miranda Evandro,Henrique Groenner Barbosa Bruno,Henrique Godinho Silva Sergio,Augusto Ussi Monti Cassio,Yue Phin Tng David,Rezende Gomide Lucas

Funder

CAPES

CEMIG

Universidade Federal de Lavras

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Nature and Landscape Conservation,Forestry

Reference112 articles.

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2. Köppen’s climate classification map for Brazil;Alvares;Meteorol. Zeitschrift,2013

3. The linear random forest algorithm and its advantages in machine learning assisted logging regression modeling;Ao;J. Pet. Sci. Eng.,2019

4. Two hybrid wrapper-filter feature selection algorithms applied to high-dimensional microarray experiments;Apolloni;Appl. Soft Comput.,2016

5. A Bolasso based consistent feature selection enabled random forest classification algorithm: An application to credit risk assessment;Arora;Appl. Soft Comput.,2020

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