Paper Info
Title
Attribute reduction of rough sets in mining market value functions
Abstract
The linear model of market value functions is a new method for direct marketing. Just like other methods in direct marketing, attribute reduction is very important to deal with large databases. We apply the algorithm of attribute reduction, which is based on the combination of rough set theory with the boosting algorithm, to the linear model of market value functions. Experimental results compared with the ELSA/ANN model show that the proposed algorithms can be used effectively in the linear model of market value functions.
Year
DOI
Venue
2003
10.1109/WI.2003.1241242
Web Intelligence
Keywords
Field
DocType
rough set theory,linear model,elsa/ann model,boosting algorithm,market value functions,attribute reduction,ann model show,marketing,rough sets,proposed algorithm,direct marketing,data mining,market value function,very large databases,large database,large databases,new method,mining market value functions,rough set,market value
Data mining,Computer science,Linear model,Direct marketing,Rough set,Boosting (machine learning),Artificial intelligence,Market value,Dominance-based rough set approach,Machine learning
Conference
ISBN
Citations 
PageRank 
0-7695-1932-6
7
0.60
References 
Authors
7
5
Name
Order
Citations
PageRank
Jiajin Huang16915.70
Chunnian Liu256161.58
Chuangxin Ou3353.06
Y. Y. Yao49707674.28
Ning Zhong52907300.63