Results 11 to 20 of about 6,745,293 (217)
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
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A review on longitudinal data analysis with random forest [PDF]
Briefings Bioinform., 2022 In longitudinal studies variables are measured repeatedly over time, leading to clustered and correlated observations. If the goal of the study is to develop prediction models, machine learning approaches such as the powerful random forest (RF) are often
Jianchang Hu, S. Szymczak
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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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Geometry- and Accuracy-Preserving Random Forest Proximities [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 Random forests are considered one of the best out-of-the-box classification and regression algorithms due to their high level of predictive performance with relatively little tuning.
Jake S. Rhodes +2 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
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Geoscience Frontiers, 2021 Accurate assessment of undrained shear strength (USS) for soft sensitive clays is a great concern in geotechnical engineering practice. This study applies novel data-driven extreme gradient boosting (XGBoost) and random forest (RF) ensemble learning ...
Wengang Zhang +4 more
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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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