Constructing Bayesian networks to predict uncollectible telecommunications accounts

被引:38
作者
Ezawa, KJ
Norton, SW
机构
[1] AT and T Laboratories, Murray Hill, NJ 07974
来源
IEEE EXPERT-INTELLIGENT SYSTEMS & THEIR APPLICATIONS | 1996年 / 11卷 / 05期
关键词
D O I
10.1109/64.539016
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The complexities of building models that can predict whether a customer account or transaction is collectible are greater than most current iearning systems can handle. The authors describe software that builds Bayesian network models for such predictions. They also examine how varying model parameters and hence model structure can affect predictive accuracy. © 1996 IEEE.
引用
收藏
页码:45 / 51
页数:7
相关论文
共 10 条
[1]  
[Anonymous], P INT C MACH LEARN I
[2]  
[Anonymous], [No title captured], DOI DOI 10.1016/B978-1-55860-332-5.50055-9
[3]  
Cooper G E, 1992, MACH LEARN, V4, P309
[4]  
Ezawa K., 1995, P 11 C UNC ART INT, P157
[5]  
EZAWA K, 1995, SYMBOLIC QUANTITATIV, P197
[6]  
EZAWA K, 1997, EXPERT SYSTEMS ARTIF
[7]  
LAURITZEN SL, 1988, J ROY STAT SOC B MET, V50, P157
[8]  
Pearl, 1989, MORGAN KAUFMANN SERI, DOI DOI 10.1016/C2009-0-27609-4
[9]  
SHACHTER R, 1990, UNCERTAINTY ARTIFICI, P173
[10]   BAYESIAN-ANALYSIS IN EXPERT-SYSTEMS [J].
SPIEGELHALTER, DJ ;
DAWID, AP ;
LAURITZEN, SL ;
COWELL, RG .
STATISTICAL SCIENCE, 1993, 8 (03) :219-247