Earticle

초록

영어

As the ubiquitous computing becomes popular, its applications come to real life as a form of a wide variety of ubiquitous decision support systems (UDSS). However, such ubiquity should be supported by prediction capability no matter which kind of contexts users are in. In this sense, context prediction capability, which is to predict future contexts users are going to enter sooner or later, becomes an extremely important part of ubiquitous decision support systems. This study proposes a new breed of context prediction mechanism using the Markov Blanket obtained from General Bayesian Network (GBN) as a main vehicle. To improve the prediction accuracy, ensemble of robust prediction classifiers is suggested on the basis of the GBN Markov Blanket. Three classifiers included in the ensemble mechanism are Bayesian networks, decision classifiers, and an SVM (Support Vector Machine). The proposed GBN Markov blanket-assisted ensemble classifier is applied to a real dataset of location prediction. Results were promising enough to conclude that the proposed ensemble classifier based on the GBN Markov Blanket is worthwhile for being adopted in developing a powerful context prediction purpose UDSS. Practical implications are also discussed with future research issues.

목차

Abstract
 1. Introduction
 2. Ensemble methods: Voting and stacking
 3. Empirical evaluation
  3.1. Data
  3.2. Experimental setup
  3.3. Results
 4. Discussion
 5. Concluding remarks
 References

키워드

저자

• Kun Chang Lee [ Professor of MIS at SKK Business School WCU Professor of Creativity Science at Department of Interaction Science Sungkyunkwan University ]
• Heeryon Cho [ Department of Interaction Science Sungkyunkwan University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of u- and e- Service, Science and Technology

• 간기

  격월간

• pISSN

  2005-4246

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605