| 基于多分类器的数据流中的概念漂移挖掘 |
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| 引用本文： | 孙岳,毛国君,刘旭,刘椿年.基于多分类器的数据流中的概念漂移挖掘[J].自动化学报,2008,34(1):93-97. |
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| 作者姓名： | 孙岳 毛国君 刘旭 刘椿年 |
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| 作者单位： | 1.北京市多媒体与智能软件重点实验室 北京工业大学计算机学院 北京 100022 |
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| 基金项目： | 国家自然科学基金(60496322，60496327)资助~~ |
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| 摘 要： | 数据流中概念漂移的检测是当前数据挖掘领域的重要研究分支, 近年来得到了广泛的关注. 本文提出了一种称为 M_ID4 的数据流挖掘算法. 它是在大容量数据流挖掘中, 通过尽量少的训练样本来实现概念漂移检测的快速方法. 利用多分类器综合技术, M_ID4 实现了数据流中概念漂移的增量式检测和挖掘. 实验结果表明, M_ID4 算法在处理数据流的概念漂移上表现出比已有同类算法更高的精确度和适应性.
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| 关 键 词： | 数据挖掘 数据流 概念漂移 |
| 收稿时间： | 2006-09-20 |
| 修稿时间： | 2007-07-09 |
Mining Concept Drifts from Data Streams Based on Multi-classifiers |
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| SUN Yue,MAO Guo-Jun,LIU Xu,LIU Chun-Nian.Mining Concept Drifts from Data Streams Based on Multi-classifiers[J].Acta Automatica Sinica,2008,34(1):93-97. |
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| Authors: | SUN Yue MAO Guo-Jun LIU Xu LIU Chun-Nian |
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| Affiliation: | 1.Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, School of Computer Science, Beijing University of Technology, Beijing 100022 |
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| Abstract: | Mining concept drifts from data streams is one of the most important fields in data mining.In this paper,a new mine algorithm called M_ID4 is proposed,which aims at quickly detecting drifted concepts from a large volume of data stream by using a small training data set.M_ID4 uses ensemble multi- classifiers to mine concept changes from the data streams,and its every classifier in the ensemble is an improved ID4 algorithm with an incremental way.The experimental results show that M_ID4 algorithm is of higher accuracy and better adaptability to quick drifted concepts than the popular algorithms. |
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| Keywords: | Data mining data stream concept drift |
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