Application of Adaptive Machine Learning Systems in Heterogeneous Data Environments
Journal Title: Global Academic Frontiers - Year 2024, Vol 2, Issue 3
Abstract
This paper explores the application and effectiveness of adaptive machine learning systems in heterogeneous data environments. With the diversification of data sources and types, traditional machine learning systems face numerous challenges, especially in data processing and model adaptability. Adaptive machine learning technologies optimize the capability to handle multi-source heterogeneous data by dynamically adjusting learning algorithms and model parameters, enhancing model accuracy and robustness. Research through theoretical analysis and multiple experiments demonstrates the effectiveness of adaptive systems in various application fields such as healthcare and finance, highlighting their advantages in complex data scenarios such as high noise and missing data. Future research will focus on improving model interpretability, optimizing large-scale data processing capabilities, expanding cross-domain applications, and strengthening data security and privacy protection to promote the widespread application and development of adaptive machine learning technology.
Authors and Affiliations
Xubo Wu| Independent Researcher, USA,Ying Wu| University Maine Presque Isle, USA,Xintao Li| University of Miami, USA,Zhi Ye| Elevance Health USA,Xingxin Gu| Northeastern University, USA,Zhizhong Wu| Google LLC, USA,Yuanfang Yang| Southern Methodist University, USA
Keywords
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- EP ID EP741423
- DOI https://doi.org/10.5281/zenodo.12684615
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