Paper Info
Title
Multiple intervals versus smoothing of boundaries in the discretization of performance indicators used for diagnosis in cellular networks
Abstract
Most real-world applications of diagnosis involve continuous-valued attributes, which are normally discretized before the existing classification algorithms are applied. The discretization may be based on data or on human expertise. In cellular networks the number of classified examples is very limited. Thus, the diagnosis experts should specify the boundaries of the intervals for each discretized symptom. The large number of values makes it difficult to specify precise parameters. Even if boundaries are obtained from classified examples, due to the limited number of cases, the obtained values are not very accurate. In this paper two techniques to improve the performance of diagnosis systems based on Bayesian Networks are compared. Some empirical results are presented for diagnosis in a GSM network. The first method, Smooth Bayesian Networks, is shown to be more robust to imprecise setting of boundaries. The second method, Multiple Uniform Intervals, is superior if accurately defined boundaries are available.
Year
DOI
Venue
2005
10.1007/11424925_100
ICCSA (4)
Keywords
Field
DocType
multiple interval,bayesian networks,classified example,cellular network,diagnosis system,diagnosis expert,discretized symptom,smooth bayesian networks,gsm network,large number,performance indicator,limited number,multiple uniform intervals,bayesian network
Discretization,Performance indicator,GSM,Computer science,Algorithm,Smoothing,Bayesian network,Cellular network,Statistical classification,Telephony
Conference
Volume
ISSN
ISBN
3483
0302-9743
3-540-25863-9
Citations 
PageRank 
References 
0
0.34
8
Authors
4
Name
Order
Citations
PageRank
Raquel Barco136441.12
Pedro Lazaro2504.18
L. Díez314123.21
Volker Wille413013.37