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Ensemble Framework for Big Data Stream Mining

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dc.contributor.author Khine, Phyo Thu Thu
dc.contributor.author Win, Htwe Pa Pa
dc.date.accessioned 2021-01-31T10:47:29Z
dc.date.available 2021-01-31T10:47:29Z
dc.date.issued 2020-02-28
dc.identifier.uri https://onlineresource.ucsy.edu.mm/handle/123456789/2557
dc.description.abstract The rapid development of industry enterprises, the large amount of data generated by these originalities and the exponential growth of industrial business website are the causes that lead to different types of big data and data stream problem. There are many stream data mining algorithms for classification and clustering with their specific properties and significance key features. Ensemble classifiers help to improve the best predictive performance results among these up-to-date algorithms. In ensemble methods, different kinds of classifiers and clusters are trained rather than training single classifier. Their prediction machine learning results are combined to a voting schedule. This paper presented a framework for stream data mining by taking the benefits of assembling technology based on miss classification stream data. Experiments are carried out with real world data streams. The experimental performance results are compared with the modern popular ensemble techniques such as Boosting and Bagging. The increasing in accuracy rate and the reducing in classification time can be seen from the test results. en_US
dc.language.iso en en_US
dc.publisher Proceedings of the Eighteenth International Conference On Computer Applications (ICCA 2020) en_US
dc.subject Big Data en_US
dc.subject Bagging en_US
dc.subject Boosting en_US
dc.subject Data Stream Mining en_US
dc.subject Ensemble Classifiers en_US
dc.subject Misclassification en_US
dc.subject Stream Data en_US
dc.title Ensemble Framework for Big Data Stream Mining en_US
dc.type Article en_US

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