基于属性分类和聚类的机器学习分类器群优化技术

T. Vadivu, B. Sumathi
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摘要

软件定义的网络通过静态架构解决流量的增长问题。SDN是一种分离网络结构的标准。服务质量与网络流量一起用于传输高带宽和多媒体信息。将分数阶达尔文优化算法(FODPSO)与粒子群优化算法相结合,提高了检测精度。本文对不同的分类算法进行了比较,以获得更好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Swarm Optimization techniques using Attribute assortment and Clustering for Machine Learning Classifiers
Software defined Networking addresses the growth of traffic with static architectures. SDN is a standard which separates the network structures. Quality of Service is used with network traffic to transfer high bandwidth and multimedia information. Fractional Order Darwinian optimization (FODPSO) is used with Particle Swarm Optimization algorithm to enhance the detection accuracy. In this research paper, comparison of different Classification algorithms are used to achieve better performance.
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