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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, Kumar Barik Manish, Nayeemulla Khan A   +2 more
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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, Alireza Abadi, Soheila Khodakarimd   +2 more
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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, Ashish Phophalia, Suman K. Mitra   +2 more
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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, Milan Kilibarda, Gerard B.M. Heuvelink, Mladen Nikolić, Branislav Bajat   +4 more
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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
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Portfolio Selection Using Random Forest Algorithm

International Journal of Computer Engineering and Data Science, 2022
Portfolio selection has long been a main topic in finance.  What stocks should one invest in? How much should one allocate to each stock to maximize gain and minimize risk?
Daname KOLANI
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Feature-Weighting and Clustering Random Forest

International Journal of Computational Intelligence Systems, 2020
Classical random forest (RF) is suitable for the classification and regression tasks of high-dimensional data. However, the performance of RF may be not satisfied in case of few features, because univariate split method cannot bring more diverse ...
Zhenyu Liu, Tao Wen, Wei Sun, Qilong Zhang   +3 more
doaj   +1 more source

Ransomware Detection using Random Forest Technique

ICT Express, 2020
Nowadays, the ransomware became a serious threat challenge the computing world that requires an immediate consideration to avoid financial and moral blackmail. So, there is a real need for a new method that can detect and stop this type of attack.
Ban Mohammed Khammas
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