A novel method for extension transformation knowledge discovering

Authors:
Xingsen Li;Zhongbiao Xiang;Haolan Zhang;Zhengxiang Zhu
Affiliations:
School of Management, Ningbo Institute of Technology, Zhejiang University, Ningbo, China;School of Management, Zhejiang University, Hangzhou, China;School of Management, Ningbo Institute of Technology, Zhejiang University, Ningbo, China;Graduate School, National Defence University PLA China, Beijing, China
Venue:
APWeb'12 Proceedings of the 14th international conference on Web Technologies and Applications
Year:
2012

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Abstract

On the foundation of analyzing the existing classification, an acquisition method of extension transformation knowledge based on Decision Tree classification has been proposed The new-bored method re-mines and transforms the decision tree rules to "can't to can, not to yes" strategy which aims to provide targeted decision-making on the transformation of the customer churn by flexible use of the extension set and extension transformation theory. Its practice in a web company has proved that this method is highly feasible, and also has the reference value for other methods research based on Extenics.