Results 31 to 40 of about 67,103 (261)
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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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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, 2023 The wealth of data being gathered about humans and their surroundings drives new machine learning applications in various fields. Consequently, more and more often, classifiers are trained using not only numerical data but also complex data objects. For example, multi-omics analyses attempt to combine numerical descriptions with distributions, time ...
Maciej Piernik +2 more
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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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On Oblique Random Forests [PDF]
, 2011 In his original paper on random forests, Breiman proposed two different decision tree ensembles: one generated from "orthogonal" trees with thresholds on individual features in every split, and one from "oblique" trees separating the feature space by randomly oriented hyperplanes.
Bjoern H. Menze +4 more
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Knowledge-Based SystemsThe Random Forest (RF) classifier is often claimed to be relatively well calibrated when compared with other machine learning methods. Moreover, the existing literature suggests that traditional calibration methods, such as isotonic regression, do not substantially enhance the calibration of RF probability estimates unless supplied with extensive ...
Shaker, Mohammad Hossein +1 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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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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A simple and effective approach to quantitatively characterize structural complexity
Scientific Reports, 2021 This study brings insight into interpreting forest structural diversity and explore the classification of individuals according to the distribution of the neighbours in natural forests. Natural forest communities with different latitudes and distribution
Gongqiao Zhang +3 more
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