| 流数据概念漂移的检测算法 |
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| 引用本文： | 张杰,赵峰.流数据概念漂移的检测算法[J].控制与决策,2013,28(1):29-35. |
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| 作者姓名： | 张杰 赵峰 |
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| 作者单位： | 山东科技大学 经济管理学院,山东 青岛 266590 |
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| 基金项目： | 中国博士后基金项目(20100481284);全国统计科研计划重点项目(2011LZ048);山东省优秀中青年科学家科研奖励基金项目(BS2012SF024) |
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| 摘 要： | 鉴于流数据具有实时、连续、有序和无限等特点,使用近似方法便可检测连续分时段的流数据序列,基于此,运用目标分布数据,结合相似分布理论,提出了利用 Tr-OEM 算法对流数据中的概念漂移现象进行检测.该算法能够动态地判断流数据概念漂移的发生,自适应地优化概念漂移的检测值,适用于不同类型的流数据.通过分析和实验仿真可以表明,该算法在处理流数据概念漂移时具有较好的适应性.
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| 关 键 词： | 流数据 概念漂移 检测 数据挖掘 |
| 收稿时间： | 2011/9/5 0:00:00 |
| 修稿时间： | 2012/4/1 0:00:00 |
Detecting algorithm of concept drift from stream data |
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| ZHANG Jie,ZHAO Feng.Detecting algorithm of concept drift from stream data[J].Control and Decision,2013,28(1):29-35. |
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| Authors: | ZHANG Jie ZHAO Feng |
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| Affiliation: | (School of Economic and Management,Shandong University of Science and Technology,Qingdao 266590,China.) |
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| Abstract: | Based on the stream data with the characters such as real-time, continuous, orderly and unlimited, the continuoustime
data sequence can be detected by using the approximate method. Based on this, making use of samples not only from the
target distribution but also from similar distributions, Tr-OEM algorithm is proposed to detect the concept drift phenomenon
in stream data. This algorithm dynamically estimates the occurrence of concept drift in stream data, automatically determines
optimizing or reconstructing classifiers, and is applied to different types of stream data. The analysis and simulation
experiments show that the proposed algorithm has better adaptability while handling the concept drift in stream data. |
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| Keywords: | stream data concept drift detecting data mining |
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