Results 11 to 20 of about 3,227,878 (310)
Random Forest Spatial Interpolation
Remote Sensing, 2020 For many decades, kriging and deterministic interpolation techniques, such as inverse distance weighting and nearest neighbour interpolation, have been the most popular spatial interpolation techniques.
Aleksandar Sekulić +4 more
doaj +3 more sources
Ecological IndicatorsA simpler, unbiased, and comprehensive random forest (RF) model is needed to improve the accuracy of aboveground biomass (AGB) estimation. In this study, data were obtained from 128 sample plots of Pinus yunnanensis forest located in Chuxiong prefecture,
Xiaoli Zhang +11 more
doaj +3 more sources
Improved Two-View Random Forest [PDF]
Jisuanji kexue yu tansuo, 2022 Random forest (RF) is one of the most classic machine learning methods, which has been widely used. However, although there are many two-view data in reality and extensive analytical research has been carried out, the RF construction for two-view ...
XIA Xiaoqiu, CHEN Songcan
doaj +1 more source
Speaker Recognition using Random Forest [PDF]
ITM Web of Conferences, 2021 Speaker identification has become a mainstream technology in the field of machine learning that involves determining the identity of a speaker from his/her speech sample.
Khadar Nawas K +2 more
doaj +1 more source
Random-Splitting Random Forest with Multiple Mixed-Data Covariates
Journal of Biostatistics and Epidemiology, 2023 Background: The bagging (BG) and random forest (RF) are famous supervised statistical learning methods based on classification and regression trees. The BG and RF can deal with different types of responses such as categorical, continuous, etc. There are
Mohammad Fayaz +2 more
doaj +1 more source
Random Forest for video Text Amazigh [PDF]
E3S Web of Conferences, 2021 In this paper; we introduce a system of automatic recognition of Video Text Amazigh based on the Random Forest. After doing some pretreatments on the video and picture, the text is segmented into lines and then into characters.
Rachidi Youssef
doaj +1 more source
HML-RF: Hybrid Multi-Label Random Forest
IEEE Access, 2022 Multi-label classification is the supervised learning problem in which an instance is associated with a set of labels. In this, labels are correlated, and hence label dependency information plays a vital role.
Vikas Jain +2 more
doaj +1 more source
Statistical Analysis and Data Mining: The ASA Data Science Journal, 2021 AbstractsidClustering is a new random forests unsupervised machine learning algorithm. The first step in sidClustering involves what is called sidification of the features: staggering the features to have mutually exclusive ranges (called the staggered interaction data [SID] main features) and then forming all pairwise interactions (called the SID ...
Alejandro Mantero, Hemant Ishwaran
openaire +4 more sources
Expert Systems with Applications, 2023 In machine learning, some models can make uncertain and imprecise predictions, they are called evidential models. These models may also be able to handle imperfect labeling and take into account labels that are richer than the commonly used hard labels, containing uncertainty and imprecision.
Hoarau, Arthur +3 more
openaire +2 more sources
Double Cost Sensitive Random Forest Algorithm
Journal of Harbin University of Science and Technology, 2021 A Double Cost Sensitive Random Forest (DCS-RF) algorithm is proposed to solve the problem that the accuracy of a few classes is not ideal when the classifier identifies unbalanced data.
ZHOU Yan-long, SUN Guang-lu
doaj +1 more source