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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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Review of Random Survival Forest method
Journal of Biostatistics and Epidemiology, 2020 Background: Over the past years, there has been a great deal of interest in applying statistical machine learning methods to survival analysis. Ensemble-based methods, especially random survival forest, have been developed in various fields, especially ...
Majid Rezaei +4 more
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Sentiment Analysis With Sarcasm Detection On Politician’s Instagram
IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 2021 Sarcasm is one of the problem that affect the result of sentiment analysis. According to Maynard and Greenwood (2014), performance of sentiment analysis can be improved when sarcasm also identified. Some research used Naïve Bayes and Random Forest method
Aisyah Muhaddisi +2 more
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An AUC-based Permutation Variable Importance Measure for Random Forests [PDF]
, 2012 The random forest (RF) method is a commonly used tool for classification with high dimensional data as well as for ranking candidate predictors based on the so-called random forest variable importance measures (VIMs).
Silke Janitza +5 more
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Unsupervised random forest for affinity estimation
Computational Visual Media, 2021 This paper presents an unsupervised clustering random-forest-based metric for affinity estimation in large and high-dimensional data. The criterion used for node splitting during forest construction can handle rank-deficiency when measuring cluster ...
Yunai Yi +5 more
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International Journal of Approximate Reasoning, 2010 AbstractWhen individual classifiers are combined appropriately, a statistically significant increase in classification accuracy is usually obtained. Multiple classifier systems are the result of combining several individual classifiers. Following Breiman’s methodology, in this paper a multiple classifier system based on a “forest” of fuzzy decision ...
Piero P. Bonissone +3 more
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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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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
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Machine Learning, 2020 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Sunwoo Han, Hyunjoong Kim, Yung-Seop Lee
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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
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