Random Forest Classifier for Distributed Multi-plant Order Allocation

Wang, Sihan; Ren, Wendi; Zhang, Yifan; Liang, Feng

doi:10.1007/978-981-13-3402-3_14

Cited by 3 publications

(2 citation statements)

References 10 publications

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“…For each construction of a tree, the decision at a node is made according to a subset of variables drawn at random. Then, we use all the decision trees produced to make the prediction, with a majority vote for the classification, predicted variable of type factor [24]. Figure 2 illustrates the construction architecture of an RF model.…”

Section: B Machine Learning Algorithmsmentioning

confidence: 99%

Optimization of Machine Learning Algorithms Hyper-Parameters for Improving the Prediction of Patients Infected with COVID-19

Hamida

Gannour

Cherradi

et al. 2020

2020 IEEE 2nd International Conference on Electronics, Control, Optimization and Computer Science (ICECOCS)

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In the modeling domain, the selection of appropriate hyper-parameters for classification or prediction algorithms is a difficult task, which has an impact on generalization capacity and classifier performance. In this paper, we compared the performance of five Machine Learning (ML) algorithms from different categories namely: SVM, AdaBoost, Random Forest, XGBoost and Decision Tree. In the first experiment, we adopt a default setting of each model for training and testing. In the second experiment, we use the GridSearch function to find an optimal configuration of the model. The experiments are performed on dataset of anonymous patients with or without COVID-19 disease. The used dataset is obtained from the Albert Einstein Hospital in Sao Paulo, Brazil. To evaluate the reached results, we used different performance evaluation metrics such as: accuracy, precision, recall, AUC and F1-score. The results of the proposed approach have shown that the optimization of the hyperparameters of the studied learning models leads to an improvement of 18% in terms of Recall.

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Section: B Machine Learning Algorithmsmentioning

confidence: 99%

Optimization of Machine Learning Algorithms Hyper-Parameters for Improving the Prediction of Patients Infected with COVID-19

Hamida

Gannour

Cherradi

et al. 2020

2020 IEEE 2nd International Conference on Electronics, Control, Optimization and Computer Science (ICECOCS)

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“…A final decision is taken through a majority rule, i.e. by measuring which prediction was made by most of the trees (Breiman, 2001, Liaw and Wiener, 2002, Wang et al, 2019a. The following graph provides a simplified depiction of the process portrayed above.…”

Section: Image Classificationmentioning

confidence: 99%

Derivation of forest inventory parameters from high-resolution satellite imagery for the Thunkel area, Northern Mongolia. A comparative study on various satellite sensors and data analysis techniques.

Vogt¹

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Automatic sentiment analysis of public opinion on nuclear energy

Xu¹,

Tang

Zhang

et al. 2022

Kerntechnik

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Opinion mining and sentiment analysis based on social media has been developed these years, especially with the popularity of social media and the development of machine learning. But in the community of nuclear engineering and technology, sentiment analysis is seldom studied, let alone the automatic analysis by using machine learning algorithms. This work concentrates on the public sentiment mining of nuclear energy in German-speaking countries based on the public comments of nuclear news in social media by using the automatic methodology, since compared with the news itself, the comments are closer to the public real opinions. The results showed that majority comments kept in neutral sentiment. 23% of comments were in positive tones, which were approximate 4 times those in negative tones. The concerning issues of the public are the innovative technology development, safety, nuclear waste, accidents and the cost of nuclear power. Decision tree, random forest and long short-term memory networks (LSTM) are adopted for the automatic sentiment analysis. The results show that all of the proposed methods can be applied in practice to some extent. But as a deep learning algorithm, LSTM gets the highest accuracy approximately 85.6% with also the best robustness of all.

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Random Forest Classifier for Distributed Multi-plant Order Allocation

Cited by 3 publications

References 10 publications

Optimization of Machine Learning Algorithms Hyper-Parameters for Improving the Prediction of Patients Infected with COVID-19

Optimization of Machine Learning Algorithms Hyper-Parameters for Improving the Prediction of Patients Infected with COVID-19

Derivation of forest inventory parameters from high-resolution satellite imagery for the Thunkel area, Northern Mongolia. A comparative study on various satellite sensors and data analysis techniques.

Automatic sentiment analysis of public opinion on nuclear energy

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