Selected article for: "classification tree and random forest"
Author: Sina F. Ardabili; Amir MOSAVI; Pedram Ghamisi; Filip Ferdinand; Annamaria R. Varkonyi-Koczy; Uwe Reuter; Timon Rabczuk; Peter M. Atkinson
Title: COVID-19 Outbreak Prediction with Machine Learning
  • Document date: 2020_4_22
    • ID: nu0pn2q8_10
    Snippet: Although ML methods were used in modeling former pandemics (e.g., Ebola, Cholera, swine fever, H1N1 influenza, dengue fever, Zika, oyster norovirus [8, [34] [35] [36] [37] [38] [39] [40] [41] [42] [43] ), there is a gap in the literature for peer-reviewed papers dedicated to COVID-19. Table 1 represents notable ML methods used for outbreak prediction. These ML methods are limited to the basic methods of random forest, neural networks, Bayesian ne.....
    Document: Although ML methods were used in modeling former pandemics (e.g., Ebola, Cholera, swine fever, H1N1 influenza, dengue fever, Zika, oyster norovirus [8, [34] [35] [36] [37] [38] [39] [40] [41] [42] [43] ), there is a gap in the literature for peer-reviewed papers dedicated to COVID-19. Table 1 represents notable ML methods used for outbreak prediction. These ML methods are limited to the basic methods of random forest, neural networks, Bayesian networks, Naïve Bayes, genetic programming and classification and regression tree (CART). Although ML has long been established as a standard tool for modeling natural disasters and weather forecasting [44, 45] , its application in modeling outbreak is still in the early stages. More sophisticated ML methods (e.g., hybrids, ensembles) are yet to be explored. Consequently, the contribution of this paper is to explore the application of ML for modeling the COVID-19 pandemic. This paper aims to investigate the generalization ability of the proposed ML models and the accuracy of the proposed models for different lead-times. The rest of this paper is organized as follows. Section two describes the methods and materials. The results are given in section three. Sections four and five present the discussion and the conclusions, respectively.

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