基于流记录偏好度的多分类器融合流量识别模型

摘要

The concept of multi-classifier fusion was introduced which can improve the classification accuracy and over-come the disadvantage of single classifier. DS theory was introduced into decision module of traffic classification and preference and timeliness was proposed. After analyzing multi-classifier model by simulation, the results show the new classifier model can overcome one sidedness of single classifier, depending on multiple evidences to optimize the traffic results.%通过将证据理论引入到流量分类的决策模块中，提出了偏好度和时效度权值，并通过实测数据对多分类器识别模型进行验证，其结果表明该模型较好的克服了单分类器的片面性，通过对多个证据的融合来优化识别的结果。