Regression Trees With Fused Leaves
Fuente:
Wiley Open Access
Salvato in:
| Autori principali: | , , , , , , |
|---|---|
| Natura: | Artículo Open Access |
| Pubblicazione: |
Wiley
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1867002683921006592 |
|---|---|
| author | Xiaogang Su Lei Liu Lili Liu Ruiwen Zhou Guoqiao Wang Elise Dusseldorp Tianni Zhou |
| author_facet | Xiaogang Su Lei Liu Lili Liu Ruiwen Zhou Guoqiao Wang Elise Dusseldorp Tianni Zhou Xiaogang Su Lei Liu Lili Liu Ruiwen Zhou Guoqiao Wang Elise Dusseldorp Tianni Zhou |
| collection | Wiley Open Access |
| contents | Regression Trees With Fused Leaves Xiaogang Su Lei Liu Lili Liu Ruiwen Zhou Guoqiao Wang Elise Dusseldorp Tianni Zhou Statistics in Medicine ABSTRACTWe propose a novel regression tree method named “TreeFuL,” an abbreviation for ‘Tree with Fused Leaves.’ TreeFuL innovatively combines recursive partitioning with fused regularization, offering a distinct approach to the conventional pruning method. One of TreeFuL's noteworthy advantages is its capacity for cross‐validated amalgamation of non‐neighboring terminal nodes. This is facilitated by a leaf coloring scheme that supports tree shearing and node amalgamation. As a result, TreeFuL facilitates the development of more parsimonious tree models without compromising predictive accuracy. The refined model offers enhanced interpretability, making it particularly well‐suited for biomedical applications of decision trees, such as disease diagnosis and prognosis. We demonstrate the practical advantages of our proposed method through simulation studies and an analysis of data collected in an obesity study. 10.1002/sim.10272 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| doi_str_mv | 10.1002/sim.10272 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_sim_10272 |
| institution | Wiley Open Access |
| license_str_mv | http://onlinelibrary.wiley.com/termsAndConditions#vor |
| publishDate | 2024 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | Regression Trees With Fused Leaves Xiaogang Su Lei Liu Lili Liu Ruiwen Zhou Guoqiao Wang Elise Dusseldorp Tianni Zhou Statistics in Medicine Regression Trees With Fused Leaves Xiaogang Su Lei Liu Lili Liu Ruiwen Zhou Guoqiao Wang Elise Dusseldorp Tianni Zhou Statistics in Medicine ABSTRACTWe propose a novel regression tree method named “TreeFuL,” an abbreviation for ‘Tree with Fused Leaves.’ TreeFuL innovatively combines recursive partitioning with fused regularization, offering a distinct approach to the conventional pruning method. One of TreeFuL's noteworthy advantages is its capacity for cross‐validated amalgamation of non‐neighboring terminal nodes. This is facilitated by a leaf coloring scheme that supports tree shearing and node amalgamation. As a result, TreeFuL facilitates the development of more parsimonious tree models without compromising predictive accuracy. The refined model offers enhanced interpretability, making it particularly well‐suited for biomedical applications of decision trees, such as disease diagnosis and prognosis. We demonstrate the practical advantages of our proposed method through simulation studies and an analysis of data collected in an obesity study. 10.1002/sim.10272 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| title | Regression Trees With Fused Leaves |
| topic | Statistics in Medicine |
| url | https://onlinelibrary.wiley.com/doi/10.1002/sim.10272 |