期刊
KNOWLEDGE-BASED SYSTEMS
卷 24, 期 8, 页码 1151-1158出版社
ELSEVIER
DOI: 10.1016/j.knosys.2011.04.011
关键词
Bayes theorem; Uncertain data; Classification; Probabilistic theory; Statistical theory
资金
- Fundamental Research Funds for the Central Universities
- Renmin University of China [10XNJ048]
Data uncertainty can be caused by numerous factors such as measurement precision limitations, network latency, data staleness and sampling errors. When mining knowledge from emerging applications such as sensor networks or location based services, data uncertainty should be handled cautiously to avoid erroneous results. In this paper, we apply probabilistic and statistical theory on uncertain data and develop a novel method to calculate conditional probabilities of Bayes theorem. Based on that, we propose a novel Bayesian classification algorithm for uncertain data. The experimental results show that the proposed method classifies uncertain data with potentially higher accuracies than the Naive Bayesian approach. It also has a more stable performance than the existing extended Naive Bayesian method. (C) 2011 Elsevier B.V. All rights reserved.
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