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

2022 ◽  
Vol 504 ◽  
pp. 119828
Author(s):  
Evandro Nunes Miranda ◽  
Bruno Henrique Groenner Barbosa ◽  
Sergio Henrique Godinho Silva ◽  
Cassio Augusto Ussi Monti ◽  
David Yue Phin Tng ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Random Forest ◽  
Variable Selection ◽  
Tree Height ◽  
Individual Tree ◽  
Selection For

Feature selection for outcome prediction in oesophageal cancer using genetic algorithm and random forest classifier

2017 ◽  
Vol 60 ◽  
pp. 42-49 ◽  
Author(s):  
Desbordes Paul ◽  
Ruan Su ◽  
Modzelewski Romain ◽  
Vauclin Sébastien ◽  
Vera Pierre ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Random Forest ◽  
Oesophageal Cancer ◽  
Outcome Prediction ◽  
Random Forest Classifier ◽  
Selection For

Random forest-based approach for physiological functional variable selection for driver’s stress level classification

2018 ◽  
Vol 28 (1) ◽  
pp. 157-185 ◽  
Author(s):  
Neska El Haouij ◽  
Jean-Michel Poggi ◽  
Raja Ghozi ◽  
Sylvie Sevestre-Ghalila ◽  
Mériem Jaïdane
Keyword(s):  
Random Forest ◽  
Variable Selection ◽  
Stress Level ◽  
Functional Variable ◽  
Selection For

APPI(+)-FTICR mass spectrometry coupled to partial least squares with genetic algorithm variable selection for prediction of API gravity and CCR of crude oil and vacuum residues

Fuel ◽  
2017 ◽  
Vol 193 ◽  
pp. 39-44 ◽  
Author(s):  
Diana Catalina Palacio Lozano ◽  
Jorge Armando Orrego-Ruiz ◽  
Rafael Cabanzo Hernández ◽  
Jáder Enrique Guerrero ◽  
Enrique Mejía-Ospino
Keyword(s):  
Mass Spectrometry ◽  
Genetic Algorithm ◽  
Variable Selection ◽  
Crude Oil ◽  
Least Squares ◽  
Partial Least Squares ◽  
Fticr Mass Spectrometry ◽  
Selection For

scholarly journals Genetic Variation and Tree Improvement of Konishii fir (Cunninghamia lanceolata (Lamb.) Hook. var. konishii) in Taiwan

2011 ◽  
Vol 60 (1-6) ◽  
pp. 196-205 ◽  
Author(s):  
Jeng-Der Chung ◽  
Gordon Nigh ◽  
Ching-Te Chien ◽  
Cheng C. Ying
Keyword(s):  
Genetic Parameters ◽  
Tree Height ◽  
Tree Improvement ◽  
Cunninghamia Lanceolata ◽  
Tree Plantations ◽  
Average Height ◽  
Individual Tree ◽  
Candidate Species ◽  
Variable Species ◽  
Selection For

AbstractWe analyzed a 21-year old progeny test of Konishii fir (Cunninghamia lanceolata(Lamb.) Hook. var.konishii) involving 75 families. Tree height and diameter at breast height (DBH) were periodically recorded. At age 21, average height, DBH, and volume were 15.2 m, 20.2 cm, and 278 dm3, respectively. At this age, family accounted for 9, 12, and 11% of the total variance in height, DBH and volume, respectively. Also at age 21, individual tree heritability was 0.35, 0.49, and 0.45 for height, DBH and volume, respectively, and family heritability was 0.53, 0.69, and 0.66 for the three respective characteristics. The age trend for all genetic parameters was more stable for DBH than for height and volume. Family (backward) selection for DBH at age 21 resulted in a 9.6% gain and indirectly 5.1 and 21.0% gains for height and volume, respectively, compared to 5.2 and 20.1% gains for height and volume, respectively, when selection for these characteristics is done directly. DBH is an effective proxy trait for selection in growth. DBH is also less susceptible than height to typhoon damage, which frequently afflicts tree plantations in Taiwan. Therefore, DBH should be considered as the primary trait for selection for Konishii fir in Taiwan. Konishii fir is a genetically variable species despite its limited geographic range, and is fast growing which makes it a viable candidate species for tree improvement.

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