On an Improved SPRINT Data Stream Online Classification Algorithm

被引：0
|
作者：
Zhou, Hong [1 ]
Fu, Chunyan [1 ]
Xue, Jiamei [1 ]
Zhi, Yuan [1 ]
Liu, Jingshun [1 ]
机构：
[1] JiaMuSi Univ, Coll Informat Sci & Elect Technol, Jiamusi 154007, Peoples R China
来源：
ADVANCES IN MANUFACTURING SCIENCE AND ENGINEERING, PTS 1-4 | 2013年 / 712-715卷
关键词：
classification algorithms; SPRINT; Entropy; Bayesian; Decision tree;
D O I：
10.4028/www.scientific.net/AMR.712-715.2648
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Aiming at the characteristics of data stream, the paper presents an incremental decision tree algorithm based on binary-attribute tree on the basis of SPRINT algorithm. The attribute set of this improved algorithm adopts the maximum entropy attribute classification and dynamic storage method of Bayesian method. By using this improved algorithm, static organization form of candidate attributes set for traditional SPRINT algorithm has been changed and it is much more suitable for concept drift and reduces the time complexity for new sampling insertion and best division node selection as well as saves storage space and increases classification efficiency.
引用
收藏
页码：2648 / 2652
页数：5
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