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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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Tuning parameters in random forests
ESAIM: Proceedings and Surveys, 2017 Breiman's (2001) random forests are a very popular class of learning algorithms often able to produce good predictions even in high-dimensional frameworks, with no need to accurately tune its inner parameters.
Scornet Erwan
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Aggregated Recommendation through Random Forests
The Scientific World Journal, 2014 Aggregated recommendation refers to the process of suggesting one kind of items to a group of users. Compared to user-oriented or item-oriented approaches, it is more general and, therefore, more appropriate for cold-start recommendation.
Heng-Ru Zhang, Fan Min, Xu He
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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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Soil Mapping Based on the Integration of the Similarity-Based Approach and Random Forests
Land, 2020 Digital soil mapping (DSM) is currently the primary framework for predicting the spatial variation of soil information (soil type or soil properties). Random forests and similarity-based methods have been used widely in DSM.
Desheng Wang, A-Xing Zhu
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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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Conditional variable importance for random forests
BMC Bioinformatics, 2008 Background Random forests are becoming increasingly popular in many scientific fields because they can cope with "small n large p" problems, complex interactions and even highly correlated predictor variables.
Augustin Thomas +4 more
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Journal of Applied Mathematics, 2012 Random forests are known to be good for data mining of classification tasks, because random forests are robust for datasets having insufficient information possibly with some errors. But applying random forests blindly may not produce good results, and a
Hyontai Sug
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Classification of GLM Flashes Using Random Forests
Earth and Space Science, 2021 [The Geostationary Lightning Mapper (GLM) detects total lightning continuously from space, and does not distinguish intra‐cloud (IC) from cloud‐to‐ground (CG) lightning. This research focuses on differentiating CG and IC lightning detected by GLM using a
Jacquelyn Ringhausen +3 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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