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Feature selection to increase the random forest method performance on high dimensional data
IJAIN (International Journal of Advances in Intelligent Informatics), 2020 Random Forest is a supervised classification method based on bagging (Bootstrap aggregating) Breiman and random selection of features. The choice of features randomly assigned to the Random Forest makes it possible that the selected feature is not ...
Maria Irmina Prasetiyowati +2 more
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World Electric Vehicle Journal, 2023 With the continuous development of electric vehicle (EV) technology, there is an increasing need to analyze the factors influencing customers’ purchase intentions. According to the data of customers’ vehicle experience evaluation and personal information,
Jian Chen +6 more
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Random forest method for estimation of brake specific fuel consumption
Scientific Reports, 2023 The internal combustion engine is a widely used power equipment in various fields, and its energy utilization is measured using brake specific fuel consumption (BSFC).
Qinsheng Yun +3 more
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A New Random Forest Method for One-Class Classification [PDF]
, 2012 We propose a new one-class classification method, called One Class Random Forest, that is able to learn from one class of samples only. This method, based on a random forest algorithm and an original outlier generation procedure, makes use of the ensemble learning mechanisms offered by random forest algorithms to reduce both the number of artificial ...
Désir, Chesner +3 more
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A New Noisy Random Forest Based Method for Feature Selection
Cybernetics and Information Technologies, 2021 Feature selection is an essential pre-processing step in data mining. It aims at identifying the highly predictive feature subset out of a large set of candidate features.
Akhiat Yassine +3 more
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Hydrology Research, 2023 A better understanding of the distribution of nonaqueous phase liquid (NAPL) plumes is of great importance to groundwater pollution remediation and control.
Litang Hu +3 more
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Hybrid Semantic Service Matchmaking Method Based on a Random Forest
Tsinghua Science and Technology, 2020 Semantic Service Matchmaking (SSM) can be leveraged for mining the most suitable service to accommodate a diversity of user demands. However, existing research on SSM mostly involves logical or non-logical matching, leading to unavoidable false-positive ...
Wei Jiang +3 more
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Remote Sensing, 2022 Quantifying changes in the vulnerability of vegetation to various drought stresses in different seasons is important for rational and effective ecological conservation and restoration.
Xiaoting Wei +9 more
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Perfect sampling methods for random forests [PDF]
Advances in Applied Probability, 2008 A weighted graphGis a pair (V, ℰ) containing vertex setVand edge set ℰ, where each edgee∈ ℰ is associated with a weightWe. A subgraph ofGis a forest if it has no cycles. All forests on the graphGform a probability space, where the probability of each forest is proportional to the product of the weights of its edges.
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Best Student Classification using Ensemble Random Forest Method
Sistemasi: Jurnal Sistem InformasiEducation at Madrasah Aliyah Negeri 1 Medan considers religious values, ethics, leadership, and participation in extracurricular activities as an integral part of student character building.
Ricky Aulia Mrg +1 more
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