Results 31 to 40 of about 338,729 (282)
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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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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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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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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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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Consistency of random forests [PDF]
, 2015 Random forests are a learning algorithm proposed by Breiman [Mach. Learn. 45 (2001) 5--32] that combines several randomized decision trees and aggregates their predictions by averaging. Despite its wide usage and outstanding practical performance, little
Biau, Gérard +2 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.
Bernard, Simon +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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