JCP 2013 Vol.8(11): 2966-2971 ISSN: 1796-203X
doi: 10.4304/jcp.8.11.2966-2971
doi: 10.4304/jcp.8.11.2966-2971
A Two-step Bayes Method for Spatial Drift in Consumer Classification
Liancai Hao, Peng Zou, and Yijun Li
School of Management, Harbin Institute of Technology, P. R. China, 150001
Abstract—In the field of data mining, spatial drift refers to the data used to develop the model consists of only one part of the population, and the differences among the samples or between sample and population are unknown. This paper proposes a two-step Bayes method to improve adaptability for different region samples, which also maintains high model accuracy. The new method first groups region based on similarity, second, sets a model structure without parameters for populations or large samples with good data quality, and then trains parameters using samples in same region group. This method builds a estimation model, proving the method by showing how it can to some extent solve the uncertainty of consumer classification.
Index Terms—spatial drift, consumer classification, bayes network
Abstract—In the field of data mining, spatial drift refers to the data used to develop the model consists of only one part of the population, and the differences among the samples or between sample and population are unknown. This paper proposes a two-step Bayes method to improve adaptability for different region samples, which also maintains high model accuracy. The new method first groups region based on similarity, second, sets a model structure without parameters for populations or large samples with good data quality, and then trains parameters using samples in same region group. This method builds a estimation model, proving the method by showing how it can to some extent solve the uncertainty of consumer classification.
Index Terms—spatial drift, consumer classification, bayes network
Cite: Liancai Hao, Peng Zou, and Yijun Li, " A Two-step Bayes Method for Spatial Drift in Consumer Classification," Journal of Computers vol. 8, no. 11, pp. 2966-2971, 2013.
General Information
ISSN: 1796-203X
Abbreviated Title: J.Comput.
Frequency: Bimonthly
Abbreviated Title: J.Comput.
Frequency: Bimonthly
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Nina Lee
Abstracting/ Indexing: DBLP, EBSCO, ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat,etc
E-mail: jcp@iap.org
-
Nov 14, 2019 News!
Vol 14, No 11 has been published with online version [Click]
-
Mar 20, 2020 News!
Vol 15, No 2 has been published with online version [Click]
-
Dec 16, 2019 News!
Vol 14, No 12 has been published with online version [Click]
-
Sep 16, 2019 News!
Vol 14, No 9 has been published with online version [Click]
-
Aug 16, 2019 News!
Vol 14, No 8 has been published with online version [Click]
- Read more>>