Balancing efficiency and accuracy: incremental learning as a key to big data processing
DOI:
https://doi.org/10.31649/1681-7893-2024-48-2-45-57Keywords:
incremental learning, Big Data, machine learning, streaming data processing, conceptual drift, catastrophic forgetting, adaptive algorithms, online learning.Abstract
The article provides a comprehensive overview of incremental learning in the context of big data processing. The basic concepts, modern approaches, and key aspects of incremental learning are considered. The advantages of this approach for processing large amounts of data are analyzed, including the efficient use of computing resources, the ability to process streaming data in real time, and adaptability to changes in data. The main limitations and challenges, such as the problem of "catastrophic forgetting", the difficulty of balancing new and old knowledge, dependence on the order of data arrival, and potential loss of accuracy, are investigated. An analysis of specific problems is presented, including the handling of conceptual drift, unbalanced classes, and missing features. Applications of incremental learning in various fields, including data analytics, robotics, autonomous driving, and activity recognition, are discussed. We suggest directions for future research to address the identified problems and improve the effectiveness of incremental learning in the context of big data.
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