DocumentCode
517399
Title
The Cascade Decision-Tree Improvement Algorithm Based on Unbalanced Data Set
Author
Yi, Wang
Volume
1
fYear
2010
fDate
12-14 April 2010
Firstpage
284
Lastpage
288
Abstract
In the past research, the data mining that using single classifier can not obtain satisfactory results. This paper proposed an improved decision-tree classification algorithm M-AdaBoost for solving the customers´ chruning problem. The idea of this algorithm is that using cascaded structure to construct more decision tree classifier based on AdaBoost. This tree have a better classification results according to the experimental results.
Keywords
customer relationship management; data mining; decision trees; pattern classification; M-AdaBoost; cascade decision-tree improvement algorithm; customer churning problem; data mining; improved decision-tree classification algorithm; unbalanced data set; Algorithm design and analysis; Classification algorithms; Classification tree analysis; Data mining; Decision trees; Error analysis; Logistics; Mobile communication; Mobile computing; Training data; AdaBoost; Data Mining; M-AdaBoost cascade tree;
fLanguage
English
Publisher
ieee
Conference_Titel
Communications and Mobile Computing (CMC), 2010 International Conference on
Conference_Location
Shenzhen
Print_ISBN
978-1-4244-6327-5
Electronic_ISBN
978-1-4244-6328-2
Type
conf
DOI
10.1109/CMC.2010.171
Filename
5471469
Link To Document