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An integrative multiomics random forest framework for robust biomarker discovery. [PDF]
GigascienceZhang W +4 more
europepmc +3 more sources
Improved Two-View Random Forest [PDF]
Jisuanji kexue yu tansuo, 2022 Random forest (RF) is one of the most classic machine learning methods, which has been widely used. However, although there are many two-view data in reality and extensive analytical research has been carried out, the RF construction for two-view ...
XIA Xiaoqiu, CHEN Songcan
doaj +1 more source
Speaker Recognition using Random Forest [PDF]
ITM Web of Conferences, 2021 Speaker identification has become a mainstream technology in the field of machine learning that involves determining the identity of a speaker from his/her speech sample.
Khadar Nawas K +2 more
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Random Shapley Forests: Cooperative Game-Based Random Forests With Consistency [PDF]
IEEE Transactions on Cybernetics, 2022 The original random forests (RFs) algorithm has been widely used and has achieved excellent performance for the classification and regression tasks. However, the research on the theory of RFs lags far behind its applications. In this article, to narrow the gap between the applications and the theory of RFs, we propose a new RFs algorithm, called random
Jianyuan Sun +5 more
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Statistical Analysis and Data Mining: The ASA Data Science Journal, 2021 AbstractsidClustering is a new random forests unsupervised machine learning algorithm. The first step in sidClustering involves what is called sidification of the features: staggering the features to have mutually exclusive ranges (called the staggered interaction data [SID] main features) and then forming all pairwise interactions (called the SID ...
Alejandro Mantero, Hemant Ishwaran
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Random-Splitting Random Forest with Multiple Mixed-Data Covariates
Journal of Biostatistics and Epidemiology, 2023 Background: The bagging (BG) and random forest (RF) are famous supervised statistical learning methods based on classification and regression trees. The BG and RF can deal with different types of responses such as categorical, continuous, etc. There are
Mohammad Fayaz +2 more
doaj +1 more source
HML-RF: Hybrid Multi-Label Random Forest
IEEE Access, 2022 Multi-label classification is the supervised learning problem in which an instance is associated with a set of labels. In this, labels are correlated, and hence label dependency information plays a vital role.
Vikas Jain +2 more
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Random Forest for video Text Amazigh [PDF]
E3S Web of Conferences, 2021 In this paper; we introduce a system of automatic recognition of Video Text Amazigh based on the Random Forest. After doing some pretreatments on the video and picture, the text is segmented into lines and then into characters.
Rachidi Youssef
doaj +1 more source
Generalized random forests [PDF]
The Annals of Statistics, 2019 We propose generalized random forests, a method for non-parametric statistical estimation based on random forests (Breiman, 2001) that can be used to fit any quantity of interest identified as the solution to a set of local moment equations. Following the literature on local maximum likelihood estimation, our method considers a weighted set of nearby ...
Athey, Susan +2 more
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Bioinformatics, 2008 Abstract Although the random forest classification procedure works well in datasets with many features, when the number of features is huge and the percentage of truly informative features is small, such as with DNA microarray data, its performance tends to decline significantly.
Dhammika, Amaratunga +2 more
openaire +2 more sources