Presentation 2014/1/23
Distributed Online Decision Tree Learning for Stream Data Based on Actor Model
Koichi YAMAMOTO, Kohei SAKURAI, Satoshi YAMANE,
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Abstract(in English) Because the conventional decision tree learning algorithm is not designed for the large-scale data computing, it isn't practical for stream data. VFDT is an online decision tree algorithm and it supports the analysis of stream data, but, it is difficult to treat the data flow of high frequency, and large quantity. So it is necessary to construct The load sharing system for online decision tree learning. We propose the method that map a branch in tree model to a data flow by message passing in actor model and disperse the node in the tree model to plural machines for realizing the load sharing system. The system performs processing to the data by distributing tasks and a data flow becomes the trigger of the next processing. In addition, It is able to cope with the unstable stream data because the system easily performs to cut a branch or to construct the subtree by changing the transmission of data in the message passing. We use the actor library in programming language Scala for implementation.
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Keyword(in English) Data stream mining / Online decision tree / Destributed decision tree / Actor model
Paper # SS2013-58,MSS2013-61
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Committee SS
Conference Date 2014/1/23(1days)
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Registration To Software Science (SS)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Distributed Online Decision Tree Learning for Stream Data Based on Actor Model
Sub Title (in English)
Keyword(1) Data stream mining
Keyword(2) Online decision tree
Keyword(3) Destributed decision tree
Keyword(4) Actor model
1st Author's Name Koichi YAMAMOTO
1st Author's Affiliation Presently with Natural Science & Technology, Kanazawa University()
2nd Author's Name Kohei SAKURAI
2nd Author's Affiliation Kanazawa University
3rd Author's Name Satoshi YAMANE
3rd Author's Affiliation Kanazawa University
Date 2014/1/23
Paper # SS2013-58,MSS2013-61
Volume (vol) vol.113
Number (no) 422
Page pp.pp.-
#Pages 6
Date of Issue