Predict COVID-19 Spreading With C-SMOTE

نویسندگان

چکیده

Data continuously gathered monitoring the spreading of COVID-19 pandemic form an unbounded flow data. Accurately forecasting if infections will increase or decrease has a high impact, but it is challenging because spreads and contracts periodically. Technically, data said to be imbalanced subject concept drifts signs decrements are minority class during periods, while they become majority in contraction periods other way round. In this paper, we propose case study applying Continuous Synthetic Minority Oversampling Technique (C-SMOTE), novel meta-strategy pipeline with Streaming Machine Learning (SML) classification algorithms, forecast trend. Benchmarking SML pipelinesthat use C-SMOTE against state-of-the-art methods on dataset, bring statistical evidence that models learned using better.

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ژورنال

عنوان ژورنال: Business information systems

سال: 2021

ISSN: ['2747-9986']

DOI: https://doi.org/10.52825/bis.v1i.45