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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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Applied Sciences, 2021 Omnidirectional honeybee traffic is the number of bees moving in arbitrary directions in close proximity to the landing pad of a beehive over a period of time.
Vladimir Kulyukin +2 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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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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Random Forests for Big Data [PDF]
, 2015 Big Data is one of the major challenges of statistical science and has numerous consequences from algorithmic and theoretical viewpoints. Big Data always involve massive data but they also often include online data and data heterogeneity.
Genuer, Robin +3 more
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RFDCR:Automated brain lesion segmentation using cascaded random forests with dense conditional random fields [PDF]
, 2020 Segmentation of brain lesions from magnetic resonance images (MRI) is an important step for disease diagnosis, surgical planning, radiotherapy and chemotherapy.
Chen, Gaoxiang +5 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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RANDOM FORESTS-BASED FEATURE SELECTION FOR LAND-USE CLASSIFICATION USING LIDAR DATA AND ORTHOIMAGERY [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012 The development of lidar system, especially incorporated with high-resolution camera components, has shown great potential for urban classification.
H. Guan, J. Yu, J. Li, J. Li, L. Luo
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Cooperative Profit Random Forests With Application in Ocean Front Recognition
IEEE Access, 2017 Random Forests are powerful classification and regression tools that are commonly applied in machine learning and image processing. In the majority of random classification forests algorithms, the Gini index and the information gain ratio are commonly ...
Jianyuan Sun +4 more
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Discriminating between glaucoma and normal eyes using optical coherence tomography and the 'Random Forests' classifier. [PDF]
PLoS ONE, 2014 To diagnose glaucoma based on spectral domain optical coherence tomography (SD-OCT) measurements using the 'Random Forests' method.SD-OCT was conducted in 126 eyes of 126 open angle glaucoma (OAG) patients and 84 eyes of 84 normal subjects.
Tatsuya Yoshida +6 more
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