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Bias in Random Forest Variable Importance Measures: Illustrations, Sources and a Solution [PDF]
, 2006 Variable importance measures for random forests have been receiving increased attention as a means of variable selection in many classification tasks in bioinformatics and related scientific fields, for instance to select a subset of genetic markers ...
Zeileis, Achim +11 more
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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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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
openaire +2 more sources
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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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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Continuous Dynamic Update of Fuzzy Random Forests
International Journal of Computational Intelligence Systems, 2022 Fuzzy random forests are well-known machine learning classification mechanisms based on a collection of fuzzy decision trees. An advantage of using fuzzy rules is the possibility to manage uncertainty and to work with linguistic scales.
Jordi Pascual-Fontanilles +3 more
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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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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
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Random Shapley Forests: Cooperative Game Based Random Forests with Consistency [PDF]
, 2020 The original random forests algorithm has been widely used and has achieved excellent performance for the classification and regression tasks. However, the research on the theory of random forests lags far behind its applications.
Yu, Hui +10 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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