| Logistic Regression for Evolving Data Streams Classification |
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| 作者姓名： | 尹志武 黄上腾 薛贵荣 |
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| 作者单位： | Dept.of Computer Science and Eng. Shanghai Jiaotong Univ.，Dept.of Computer Science and Eng.，Shanghai Jiaotong Univ.，Dept.of Computer Science and Eng.，Shanghai Jiaotong Univ.，Shanghai 200030，China，Shanghai 200030，China，Shanghai 200030，China |
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| 摘 要： | Logistic regression is a fast classifier and can achieve higher accuracy on small training data.Moreover,it can work on both discrete and continuous attributes with nonlinear patterns.Based on these properties of logistic regression,this paper proposed an algorithm,called evolutionary logistical regression classifier(ELRClass),to solve the classification of evolving data streams.This algorithm applies logistic regression repeatedly to a sliding window of samples in order to update the existing classifier,to keep this classifier if its performance is deteriorated by the reason of bursting noise,or to construct a new classifier if a major concept drift is detected.The intensive experimental results demonstrate the effectiveness of this algorithm.
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| 关 键 词： | 类别 后勤海退 数据流矿业 分类器 |
| 文章编号： | 1007-1172（2007）02-0197-07 |
| 修稿时间： | 2006-05-10 |
Logistic Regression for Evolving Data Streams Classification |
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| YIN Zhi-wu,HUANG Shang-teng,XUE Gui-rong.Logistic Regression for Evolving Data Streams Classification[J].Journal of Shanghai Jiaotong university,2007,12(2):197-203. |
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| Authors: | YIN Zhi-wu HUANG Shang-teng XUE Gui-rong |
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| Institution: | Dept. of Computer Science and Eng. , Shanghai Jiaotong Univ. , Shanghai 200030, China |
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| Abstract: | Logistic regression is a fast classifier and can achieve higher accuracy on small training data.Moreover,it can work on both discrete and continuous attributes with nonlinear patterns.Based on these properties of logistic regression,this paper proposed an algorithm,called evolutionary logistical regression classifier(ELRClass),to solve the classification of evolving data streams.This algorithm applies logistic regression repeatedly to a sliding window of samples in order to update the existing classifier,to keep this classifier if its performance is deteriorated by the reason of bursting noise,or to construct a new classifier if a major concept drift is detected.The intensive experimental results demonstrate the effectiveness of this algorithm. |
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| Keywords: | classification logistic regression data stream mining |
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