Combination of Feature Selection and CatBoost for Prediction: The First Application to the Estimation of Aboveground Biomass

Combination of Feature Selection and CatBoost for Prediction: The First Application to the Estimation of Aboveground Biomass

Increasing numbers of explanatory variables tend to result in information redundancy and “dimensional disaster” in the quantitative remote sensing of forest aboveground biomass (AGB). Feature selection of model factors is an effective method for improving the accuracy of AGB estimates. Machine learn...

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Bibliographic Details
Main Authors: Mi Luo, Yifu Wang, Yunhong Xie, Lai Zhou, Jingjing Qiao, Siyu Qiu, Yujun Sun
Format: Article
Language:English
Published: MDPI AG 2021-02-01
Series:Forests
Subjects:
feature selection
machine learning algorithms
ensemble learning
CatBoost
XGBoost
forest type
Online Access:https://www.mdpi.com/1999-4907/12/2/216

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https://www.mdpi.com/1999-4907/12/2/216

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