[1]白洋,王志海,孙艳歌.基于图的概念重现发现与预测[J].郑州大学学报(工学版),2017,38(04):57-64.[doi:10.13705/j.issn.1671-6833.2017.01.021]
 Baiyang,Wang Zhihai,Sun Yange.Recurring Concept Detection and Prediction Based on the Graph[J].Journal of Zhengzhou University (Engineering Science),2017,38(04):57-64.[doi:10.13705/j.issn.1671-6833.2017.01.021]
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基于图的概念重现发现与预测()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
38卷
期数:
2017年04期
页码:
57-64
栏目:
出版日期:
2017-07-18

文章信息/Info

Title:
Recurring Concept Detection and Prediction Based on the Graph
作者:
白洋王志海孙艳歌
1.北京交通大学计算机与信息技术学院,北京,100044;2.信阳师范学院计算机与信息技术学院,河南信阳464000
Author(s):
Baiyang1Wang Zhihai1Sun Yange2
1. School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044; 2. School of Computer and Information Technology, Xinyang Normal University, Xinyang, Henan 464000
关键词:
Keywords:
data streamdata miningconcept driftdrift detectionrecurring concept
DOI:
10.13705/j.issn.1671-6833.2017.01.021
文献标志码:
A
摘要:
概念漂移是数据流挖掘中具有挑战性的问题.当概念漂移发生后,原有分类模型的分类正确率会显著下降,因此需要及时发现并调整模型以适应这些改变.概念重现是概念漂移的特殊情况,然而已有的算法大多未能充分考虑这种状况.为此,提出一种能够处理重现的概念检测方法.试验结果表明,该方法能够以较低的延迟和较低的误报率检测到概念漂移,并且可以识别重现的概念,很大程度上提升了分类器的分类正确率.
Abstract:
Concept drift was a challenging problem in stream mining.When the concept drift occured,the accuracy of the original predictive model may decrease significantly.So it was necessary to put forward a feasible method to detect concept drift.Recurring concept is a special case of concept drift.However,most of existing algorithms have not taken full account of this case.This research proposed an approach to the recurring concept detection problem.Extensive experiment revealed that the method we proposed could detect not only the concept drift with relatively low delay and rate of false positive,but also the recurring concepts.Moreover,the accuracy of the classification would be greatly improved.

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