Application of Adaptive Machine Learning Systems in Heterogeneous Data Environments
DOI:
https://doi.org/10.5281/zenodo.12684615Keywords:
Adaptive Machine Learning Systems, Heterogeneous Data Environments, Data Quality, Data Integration, Deep Learning, Generalization AbilityAbstract
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.
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Copyright (c) 2024 Xubo Wu, Ying Wu, Xintao Li, Zhi Ye, Xingxin Gu, Zhizhong Wu, Yuanfang Yang (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
