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Identification of key influencing factors of health information literacy in COPD patients: a cross-sectional study using a random forest model. [PDF]

BMC Pulm Med
Wu JH, Wu JM, Huang B, Wei LL.
europepmc   +1 more source

Development of a Gait Independence Prediction Model in Patients With Stroke in a Convalescent Rehabilitation Ward: A Comparison of Decision Tree and Random Forest Models. [PDF]

Cureus
Nakao S, Motokawa T, Nakamori T.
europepmc   +1 more source

A hybrid ACO-random forest optimization framework for scalable microalgae biomass estimation using multispectral imaging. [PDF]

Environ Monit Assess
Kolawole KK, Abidin MSBZ, Kamaroddin MFB, Ramli MSAB, Mpuhus SL, Rizqi A.   +5 more
europepmc   +1 more source

Assessing the impact of waterfront trail aesthetics on psychological restoration in urban environments: a deep learning and random forest approach. [PDF]

Front Public Health
Wu Y, Wang Y, Wang J, Li J, Ding Y, Ding Y, Wu Y, Zhu Z, Xu X.   +8 more
europepmc   +1 more source

Enhancing software effort estimation with random forest tuning and adaptive decision strategies. [PDF]

Sci Rep
A G PV, K AK, S R.
europepmc   +1 more source

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A comparison of random forest based algorithms: random credal random forest versus oblique random forest

Soft Computing, 2018
Random forest (RF) is an ensemble learning method, and it is considered a reference due to its excellent performance. Several improvements in RF have been published. A kind of improvement for the RF algorithm is based on the use of multivariate decision trees with local optimization process (oblique RF).
Javier G Castellano, Joaquin Abellan, Serafin Moral Garcia   +2 more
exaly   +2 more sources