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Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2013 Vol.3(6): 473-478 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2013.V3.363

Context Inconsistency Elimination Based on User Feedback and Modified Evidence Theory

Leitao Wang, Hongji Xu, Guoxia Sun, Zhengfeng Du, Zhigang Xie, and Lina Zheng
Abstract—In dynamic and open environment, context aware system obtains context information from the dynamic, distributed and heterogeneous sources, but the context information usually has inconsistencies which would lead to inappropriate services. We proposed a new context inconsistency elimination algorithm based on user feedback and modified evidence theory in this paper. Through user feedback, each sensor’s perception precision can be acquired, and with the modified evidence theory, we can make full use of all context information and eliminate inconsistent context by adjusting the influence of every context on whole judgment based on sensor perception precision. In order to evaluate the performance of the proposed context inconsistency elimination algorithm, context aware rate is defined. The experiment results show that the proposed context inconsistency elimination algorithm can obtain the best context aware rate in most cases when the error rates of sensors are varied.

Index Terms—Adaptive service, context aware, context inconsistency elimination, user feedback

The authors are with School of Information Science and Engineering, Shandong University, Jinan, China (e-mail: hongjixu@sdu.edu.cn, zfdu@sdu.edu.cn).

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Cite:Leitao Wang, Hongji Xu, Guoxia Sun, Zhengfeng Du, Zhigang Xie, and Lina Zheng, "Context Inconsistency Elimination Based on User Feedback and Modified Evidence Theory," International Journal of Machine Learning and Computing vol.3, no. 6, pp. 473-478, 2013.

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