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BoostTree and BoostForest for Ensemble Learning.
IEEE Trans Pattern Anal Mach Intell ; 45(7): 8110-8126, 2023 Jul.
Article em En | MEDLINE | ID: mdl-37015516
ABSTRACT
Bootstrap aggregating (Bagging) and boosting are two popular ensemble learning approaches, which combine multiple base learners to generate a composite model for more accurate and more reliable performance. They have been widely used in biology, engineering, healthcare, etc. This article proposes BoostForest, which is an ensemble learning approach using BoostTree as base learners and can be used for both classification and regression. BoostTree constructs a tree model by gradient boosting. It increases the randomness (diversity) by drawing the cut-points randomly at node splitting. BoostForest further increases the randomness by bootstrapping the training data in constructing different BoostTrees. BoostForest generally outperformed four classical ensemble learning approaches (Random Forest, Extra-Trees, XGBoost and LightGBM) on 35 classification and regression datasets. Remarkably, BoostForest tunes its parameters by simply sampling them randomly from a parameter pool, which can be easily specified, and its ensemble learning framework can also be used to combine many other base learners.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: IEEE Trans Pattern Anal Mach Intell Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: IEEE Trans Pattern Anal Mach Intell Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2023 Tipo de documento: Article
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