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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
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

Predicting autism spectrum disorder severity in children based on specific language milestones: a random forest model approach. [PDF]

Child Adolesc Psychiatry Ment Health
Xiong H, Xiang X, Liu X, Yang T, Chen J, Chen J, Li T.   +6 more
europepmc   +3 more sources

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
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
doaj   +1 more source

Sentiment Analysis With Sarcasm Detection On Politician’s Instagram

IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 2021
Sarcasm is one of the problem that affect the result of sentiment analysis. According to Maynard and Greenwood (2014), performance of sentiment analysis can be improved when sarcasm also identified. Some research used Naïve Bayes and Random Forest method
Aisyah Muhaddisi, Bambang Nurcahyo Prastowo, Diyah Utami Kusumaning Putri   +2 more
doaj   +1 more source

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
doaj   +1 more source

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

RFDCR:Automated brain lesion segmentation using cascaded random forests with dense conditional random fields [PDF]

, 2020
Segmentation of brain lesions from magnetic resonance images (MRI) is an important step for disease diagnosis, surgical planning, radiotherapy and chemotherapy.
Chen, Gaoxiang, Li, Qun, Pan, Zhifang, Rekik, Islem, Shi, Fuqian, Wang, Li   +5 more
core   +2 more sources