Short Communication: Integration of General Bayesian Network and ubiquitous decision support to provide context prediction capability

Authors:
Kun Chang Lee;Heeryon Cho
Affiliations:
SKK Business School and Department of Interaction Science, Sungkyunkwan University, Seoul 110-745, South Korea;Department of Interaction Science, Sungkyunkwan University, Seoul 110-745, South Korea
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

Citing 11
Cited 0

A Bayesian Method for the Induction of Probabilistic Networks from Data

Machine Learning
Bayesian Network Classifiers

Machine Learning - Special issue on learning with probabilistic representations
Proactive computing

Communications of the ACM
Past, present, and future of decision support technology

Decision Support Systems - Special issue: Decision support systems: Directions for the next decade
Towards a Better Understanding of Context and Context-Awareness

HUC '99 Proceedings of the 1st international symposium on Handheld and Ubiquitous Computing
Inference for the Generalization Error

Machine Learning
A causal mapping approach to constructing Bayesian networks

Decision Support Systems
Review: Ambient intelligence: Technologies, applications, and opportunities

Pervasive and Mobile Computing
On the classification performance of TAN and general Bayesian networks

Knowledge-Based Systems
The WEKA data mining software: an update

ACM SIGKDD Explorations Newsletter
Human Activity Recognition and Pattern Discovery

IEEE Pervasive Computing

Quantified Score

Hi-index	12.05

Visualization

Abstract

We propose a new type of ubiquitous decision support system which is powered by a General Bayesian Network (GBN). While complicated decision support problems are plagued by complexities involved in interpreting causal relationships among decision variables, GBN has shown excellent decision support competence due to its flexible structure which allows itself to extract appropriate and robust causal relationships among target variable and related explanatory variables. The potentials of GBN, however, were not explored enough in the field of ubiquitous decision support area. Hence, we propose a new type of ubiquitous decision support mechanism named U-BASE which uses GBN for context prediction to improve decision support. To prove 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. Practical implications are fully discussed for motivating future studies.