Results 131 to 140 of about 5,516,106 (266)

Copula Approximate Bayesian Computation Using Distribution Random Forests

Stats
Ongoing modern computational advancements continue to make it easier to collect increasingly large and complex datasets, which can often only be realistically analyzed using models defined by intractable likelihood functions.
George Karabatsos
doaj   +1 more source

Two-stage approach for identifying single-nucleotide polymorphisms associated with rheumatoid arthritis using random forests and Bayesian networks [PDF]

, 2007
Yan Meng, Qiong Yang, Karen T. Cuenco, L. Adrienne Cupples, Anita L. DeStefano, Kathryn L. Lunetta   +5 more
openalex   +1 more source

Face classification by a random forest [PDF]

, 2007
Abbas Z. Kouzani, Saeid Nahavandi, Khashayar Khoshmanesh   +2 more
openalex   +1 more source

MiPred: classification of real and pseudo microRNA precursors using random forest prediction model with combined features [PDF]

, 2007
Peng Jiang, H. Wu, Wenxue Wang, Wei Ma, Xiao Sun, Zuhong Lu   +5 more
openalex   +1 more source

Enumerations of trees and forests related to branching processes and random walks

, 1998
James C. Pitman
openalex   +1 more source

An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests.

Psychological methods, 2009
C. Strobl, J. Malley, G. Tutz
semanticscholar   +1 more source

Adaptive random forests for evolving data stream classification

Machine-mediated learning, 2017
Heitor Murilo Gomes, A. Bifet, J. Read, J. P. Barddal, F. Enembreck, Bernhard Pfahringer, G. Holmes, T. Abdessalem   +7 more
semanticscholar   +1 more source

Pathway Analysis of Single-Nucleotide Polymorphisms Potentially Associated with Glioblastoma Multiforme Susceptibility Using Random Forests [PDF]

, 2008
Jeffrey S. Chang, Ru‐Fang Yeh, John K. Wiencke, Joseph L. Wiemels, Ivan Smirnov, Alexander R. Pico, Tarık Tihan, Joe Patoka, Rei Miike, Jennette D. Sison, Terri Rice, Margaret Wrensch   +11 more
openalex   +1 more source

Imputation for Random Forests

, 2017
This project introduces two new methods for imputation of missing data in random forests. The new methods are compared against other frequently used imputation methods, including those used in the randomForest package in R. To test the effectiveness of these methods, missing data are imputed into datasets that contain two missing data mechanisms ...
openaire   +3 more sources

Random Forests Relay Selector in Buffer-Aided Cooperative Networks

IEEE Access
This paper presents a data-driven Random Forest (RF) framework for the joint relay–link selection and buffer management problem in buffer-aided cooperative relay networks, with the goal of minimizing outage probability.
Mohammad Alkhawatrah
doaj   +1 more source