APPLICATION OF MACHINE LEARNING METHODS TO ENHANCE THE PERFORMANCE OF BIG DATA SORTING ALGORITHMS

The use of machine learning methods to optimize big data sorting algorithms has become an urgent research topic due to the growing volume of information and the requirements for their rapid processing. Machine learning provides opportunities to automate and improve traditional sorting methods, allowing you to reduce the cost of computing resources and time. This is achieved by analyzing the characteristics of the data and preprocessing them using classification and regression. The main advantages of using machine learning in sorting big data include improving the accuracy and adaptability of algorithms to different types of data, which is especially important for areas with large amounts of information, such as finance, medicine and logistics. Progressive machine learning algorithms such as supporting vectors, decision trees, and gradient boosting demonstrate high efficiency and potential for further development and integration.随着信息体量持续增长，且对其快速处理的需求日益提升，利用机器学习方法优化大数据排序算法已成为一项亟待开展的研究课题。机器学习可为传统排序方法的自动化升级与性能优化提供新的契机，有效降低计算资源消耗与处理时间成本。该优化效果可通过分析数据特征，并借助分类与回归手段完成数据预处理来实现。将机器学习应用于大数据排序的核心优势，在于提升算法的准确率与对不同类型数据的适配能力，这在金融、医疗、物流等信息密集型领域尤为关键。支持向量机（Support Vector Machines）、决策树（Decision Trees）、梯度提升算法（Gradient Boosting）等先进机器学习算法，已展现出优异的应用效能，且具备进一步发展与集成的广阔潜力。