Results 41 to 50 of about 2,068,010 (305)
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
doaj +1 more source
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
openaire +3 more sources
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
doaj +1 more source
, 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
openaire +3 more sources
Detecting targets hidden in random forests. [PDF]
, 2009 Military tanks, cargo or troop carriers, missile carriers or rocket launchers often hide themselves from detection in the forests. This plagues the detection problem of locating these hidden targets.
Wu, Biao +3 more
core +1 more source
IATSS ResearchThis paper presents a consolidated overview of random forest-based methods for road traffic safety research, mainly focusing on recent methodological developments tailored to particular needs in three typical crash data modelling tasks: crash frequency ...
Yingheng Zhang, Dongyu Wu
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source
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
openaire +2 more sources
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
openaire +4 more sources