Abstract
Abstract: The causes of numerous sorts of big data and data stream problems include the quick development of industry firms, the vast amount of data generated by these innovations, and the exponential growth of industrial company websites. There are numerous stream data mining algorithms for classification and grouping, each with its own unique set of attributes and important features. Ensemble classifiers aid in enhancing the greatest prediction performance results from these cutting-edge techniques. Ensemble approaches teach multiple types of classifiers and clusters rather than a single classifier. Their machine learning prediction findings are merged to form a voting schedule. This research offered a framework for stream data mining based on miss categorization stream data, utilizing the advantages of assembly technology. Real-world data streams are used in experiments. The experimental results are compared to modern popular ensemble techniques such as Boosting and Bagging. The test results show an increase in accuracy rate and decrease in classification time.
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