通信学报
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董仕1,2,3,丁伟1,2
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摘要: 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。
Abstract: The concept of multi-classifier fusion was introduced which can improve the classification accuracy and overcome 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.
董仕1,2,3,丁伟1,2. 基于流记录偏好度的多分类器融合流量识别模型[J]. 通信学报.
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https://www.infocomm-journal.com/txxb/CN/Y2013/V34/I10/17