979.txt

This complete matlab for neural network

发信人: GzLi (笑梨), 信区: DataMining
标  题: svm<1>Database Marketing
发信站: 南京大学小百合站 (Tue Jun  4 18:40:49 2002), 站内信件

Support Vector Decision Tree Methods for Database Marketing
We introduce a support vector decision tree method for customer targeting
 in the framework of large databases (database marketing). The goal is to
 provide a tool to identify the best customers based on historical data (
model development). Then this tool is used to forecast the best potential
 customers among a pool of prospects through a process of scoring. We begin
 by recursively constructing a decision tree. Each decision consists of a
 linear combination of the independent attributes. A linear program motivated
 by the support vector machine method from Vapnik's Statistical Learning 
Theory is used to construct each decision. A gainschart table is used to 
verify the goodness of fit of the targeting, the likely prospects, and the
 expected utility of profit. Successful results are given for three industrial
 problems. The linear program automatically performs dimensionality reduction
. The method consistently produced trees with a very small number of decision
 nodes. Each decision consisted of a relatively small number of attributes
. The trees produced a clear division of the population into likely prospects
, unlikely prospects, and ambiguous prospects. The largest training dataset
 tested contained 15,700 points with 866 attributes. Commercial optimization
 package used, CPLEX, is capable of solving even larger problems. 
Reference(s): 
On Support Vector Decision Trees for Database Marketing K.P. Bennett, D. 
Wu, and L. Auslender Report No. 98-100, Rensselaer Polytechnic Institute,
 Troy, NY, 1998. 
Reference link(s): 
http://www.rpi.edu/~bennek/mr98100.ps 
Data link(s): 
Data is proprietary.
Entered by: Kristin P Bennett <[e-mail]> - Thursday, September 09, 
1999 at 15:16:07 (PDT) 
Comments: The support vector decision tree performed better than C4.5. SVDT
 produced very simple trees using few attributes. 
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