DocumentCode :
2112649
Title :
An Improved Algorithm of Decision Trees for Streaming Data Based on VFDT
Author :
Li, Feixiong ; Liu, Quan
Author_Institution :
Provincial Key Lab. for Comput. Inf. Process. Technol., Soochow Univ. Suzhou, Suzhou
Volume :
1
fYear :
2008
fDate :
20-22 Dec. 2008
Firstpage :
597
Lastpage :
600
Abstract :
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.
Keywords :
Bayes methods; data mining; decision trees; pattern classification; Naive Bayes classifier; UINP approach; VFDT system; data stream mining; one pass online algorithm; uneven interval numerical pruning; very fast decision tree; Decision Trees; Naive Bayes Classifiers; Streaming Data Mining; Unequal Interval Numerical Pruning(UINP);
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Science and Engineering, 2008. ISISE '08. International Symposium on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-2727-4
Type :
conf
DOI :
10.1109/ISISE.2008.256
Filename :
4732288
Link To Document :
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