Modified EPPXGBOOST for Effective Data Stream Mining in Cloud
Main Article Content
Abstract
In today’s technology-driven landscape, the perva- sive use of online services across diverse domains has led to the generation of vast datasets, necessitating advanced data mining techniques for meaningful insights. The advent of data streams, characterized by continuous and dynamic data flows, presents a significant challenge, prompting the evolution of data stream mining. This field addresses issues such as rapid changes in streaming data and the need for quick algorithms. To tackle these challenges, an innovative approach named (Effective Privacy Preserving eXtreme Gradient Boosting) EPPXGBOOST is proposed, combining Adaptive XGBOOST for continuous learning from evolving data streams with PPXGBOOST for privacy preservation.
Article Details
How to Cite
Aniket Patel, Kiran Amin. (2024). Modified EPPXGBOOST for Effective Data Stream Mining in Cloud. International Journal on Recent and Innovation Trends in Computing and Communication, 11(11), 915–921. Retrieved from https://ijritcc.org/index.php/ijritcc/article/view/10363
Issue
Section
Articles