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按需系综的数据流分类算法研究

Study of Data Stream Classification Algorithm with On-demand Ensemble
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摘要 传统分类器系综数据流分类算法内存消耗高、计算开销大。针对该问题,提出一种按需系综分类算法。根据数据流的特点,按需动态调整分类器的个数和权值,从而保持较高分类精度、降低开销。通过对2种人工数据流的实验分析表明,该算法对隐含概念漂移的数据流分类效率及精度都有一定提升,内存开销有所降低。 Aiming at the problem of high RAM and computation consuming in traditional data stream ensemble classification algorithm,it proposes an on-demand ensemble classification algorithm,which can revises the number of classifier and their weights on demand actively,so as to achieve the purpose of reducing cost while maintaining high classification accuracy.According to the experiments on two synthetic datasets,both classification efficient and accuracy have been improved in hidden concept drifting data streams,while the memory consumption has reduced significantly.
作者 钱琳 秦亮曦
出处 《计算机工程》 CAS CSCD 2012年第5期62-63,69,共3页 Computer Engineering
基金 "十一五"国家科技支撑计划基金资助项目(2009BAH53B03)
关键词 数据流 按需系综 概念漂移 分类器系综 data stream on-demand ensemble concept drifting classifier ensemble
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参考文献6

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