Paper Info
Title
U-BASE: General Bayesian Network-Driven Context Prediction for Decision Support.
Abstract
We propose a new type of ubiquitous decision support system that is powered by a General Bayesian Network (GBN). Because complicated decision support problems are plagued by complexities when interpreting causal relationships among decision variables, GBNs have shown excellent decision support competence because of their flexible structure, which allows them to extract appropriate and robust causal relationships among target variables and related explanatory variables. The potential of GBNs, however, has not been sufficiently investigated in the field of ubiquitous decision support. Hence, we propose a new type of ubiquitous decision support mechanism called U-BASE, which uses a GBN for context prediction in order to improve decision support. To illustrate the validity of the proposed decision support mechanism, we collected a set of contextual data from college students and applied U-BASE to induce useful and robust results. The practical implications are fully discussed, and issues for future studies are suggested.
Year
DOI
Venue
2010
10.1007/978-3-642-16699-0_8
ADVANCES IN INFORMATION TECHNOLOGY
Keywords
Field
DocType
Context Prediction,General Bayesian Network,U-BASE
Decision rule,Decision tree,Intelligent decision support system,Computer science,Decision support system,Influence diagram,Artificial intelligence,R-CAST,Evidential reasoning approach,Decision engineering,Machine learning
Conference
Volume
ISSN
Citations 
114
1865-0929
0
PageRank 
References 
Authors
0.34
12
3
Name
Order
Citations
PageRank
Kun Chang Lee199494.73
Heeryon Cho2709.38
Sunyoung Lee350.80