Results 41 to 50 of about 452,858 (310)
CoRR, 2019 Space partitioning methods such as random forests and the Mondrian process are powerful machine learning methods for multi-dimensional and relational data, and are based on recursively cutting a domain. The flexibility of these methods is often limited by the requirement that the cuts be axis aligned.
Ge, S +4 more
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Pattern Recognition Letters, 2012 In this paper, we introduce a new Random Forest (RF) induction algorithm called Dynamic Random Forest (DRF) which is based on an adaptative tree induction procedure. The main idea is to guide the tree induction so that each tree will complement as much as possible the existing trees in the ensemble.
Simon Bernard 0001 +2 more
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AUC-RF: A New Strategy for Genomic Profiling with Random Forest [PDF]
, 2011 Objective: Genomic profiling, the use of genetic variants at multiple loci simultaneously for the prediction of disease risk, requires the selection of a set of genetic variants that best predicts disease status.
Luz Calle, M. +10 more
core +1 more source
Computational and Structural Biotechnology Journal, 2022 Gene-to-gene networks, such as Gene Regulatory Networks (GRN) and Predictive Expression Networks (PEN) capture relationships between genes and are beneficial for use in downstream biological analyses.
Angelica M. Walker +7 more
doaj +1 more source
Crossbreeding in Random Forest
CoRR, 2021 Ensemble learning methods are designed to benefit from multiple learning algorithms for better predictive performance. The tradeoff of this improved performance is slower speed and larger size of ensemble learning systems compared to single learning systems.
Abolfazl Nadi +2 more
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Archives of Medical Science, 2021 Introduction It is essential to predict the survival status of patients based on their prognosis. This can assist physicians in evaluating treatment decisions. Random forest is an excellent machine learning algorithm even without any modification.
Cheng Xu +4 more
doaj +1 more source
Danger: High Power! – Exploring the Statistical Properties of a Test for Random Forest Variable Importance [PDF]
, 2008 Random forests have become a widely-used predictive model in many scientific disciplines within the past few years. Additionally, they are increasingly popular for assessing variable importance, e.g., in genetics and bioinformatics.
Zeileis, Achim +3 more
core +1 more source
Crop Yield Prediction Using Improved Random Forest [PDF]
ITM Web of Conferences, 2023 Agriculture has an important role in India’s economic development. Crop productivity is affected by the rising population and the country’s ever-changing climate. Crop yield estimation is a challenge in the farming sector.
T. Padma, Sinha Dipali
doaj +1 more source
Fecal source identification using random forest
Microbiome, 2018 Background Clostridiales and Bacteroidales are uniquely adapted to the gut environment and have co-evolved with their hosts resulting in convergent microbiome patterns within mammalian species.
Adélaïde Roguet +3 more
doaj +1 more source
Journal of Biostatistics and Epidemiology, 2021 Objective: Rapid technological advances in the last century and the large amount of information have made it difficult to analyze a large number of independent variables.
Maryam Deldar +2 more
doaj +1 more source