摘要
随着大数据时代的到来,多维数据集的规模和复杂性持续增长,传统机器学习算法在处理高维数据时面临诸多挑战。本文通过构建标准化的性能评估框架,系统地比较了支持向量机、随机森林、神经网络等主流算法在不同维度数据集上的表现。实验采用5个真实数据集,维度范围从50维到10000维,从准确率、计算复杂度、内存消耗和可扩展性四个维度进行综合评估。结果表明,随机森林在中低维数据(<1000维)上表现最优,深度神经网络在超高维数据上具有明显优势,而支持向量机在特定场景下展现出了良好的泛化能力。研究为不同应用场景下的算法选择提供了定量依据,揭示了维度诅咒对算法性能的具体影响机制。
With the advent of the big data era,the scale and complexity of multidimensional datasets continue to grow,and traditional machine learning algorithms face many challenges when processing high-dimensional data.This article systematically compares the performance of mainstream algorithms such as support vector machines,random forests,and neural networks on different dimensional datasets by constructing a standardized performance evaluation framework.The experiment used 5 real datasets with dimensions ranging from 50 to 10000 dimensions,and conducted a comprehensive evaluation from four dimensions:accuracy,computational complexity,memory consumption,and scalability.The results indicate that random forests perform the best on low to medium dimensional data(<1000 dimensions),deep neural networks have significant advantages on ultra-high dimensional data,and support vector machines exhibit good generalization ability in specific scenarios.The study provides quantitative basis for algorithm selection in different application scenarios and reveals the specific impact mechanism of dimension curse on algorithm performance.
作者
要一璐
YAO Yilu(Shanxi International Business Vocational College,Taiyuan Shanxi 030006)
出处
《软件》
2025年第7期64-66,112,共4页
Software
关键词
多维数据
机器学习
算法性能
维度诅咒
大数据分析
multi-dimensional data
machine learning
algorithm performance
dimension curse
big data analysis
作者简介
要一璐(1991-),女,硕士,助教,研究方向:大数据。
