An Improved Algorithm of Decision Trees for Streaming Data Based on VFDT

被引：3
|
作者：
Li, Feixiong [1 ]
Liu, Quan [1 ]
机构：
[1] Soochow Univ, Prov Key Lab Comp Informat Proc Technol, Suzhou 215006, Peoples R China
来源：
ISISE 2008: INTERNATIONAL SYMPOSIUM ON INFORMATION SCIENCE AND ENGINEERING, VOL 1 | 2008年
关键词：
Streaming Data Mining; Decision Trees; Unequal Interval Numerical Pruning(UINP); Naive Bayes Classifiers;
D O I：
10.1109/ISISE.2008.256
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Decision tree is a good model of Classification. Recently, there has been much interest in mining streaming data. Because streaming data is large and no limited, it is unpractical that passing the entire data over more than one time. A one pass online algorithm is necessary. One of the most successful algorithms for mining data streams is VFDT(Very Fast Decision Tree). we extend the VFDT system to EVFDT(Efficient-VFDT) in two directions: (1)We present Uneven Interval Numerical Pruning (shortly UINP) approach for efficiently processing numerical attributes. (2)We use naive Bayes classifiers associated with the node to process the samples to detect the outlying samples and reduce the scale of the trees. From the experimental comparison, the two techniques significantly improve the efficiency and the accuracy of decision tree construction on streaming data.
引用
收藏
页码：597 / 600
页数：4
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