Comparison Evidential Fusion Network with Decision Tree for Reliable Contextual Information

In intelligent environments, detecting possible errors and completing missing values then deciding about the quality and validity of the sensed data are roles of context reasoning in order to improve the reliability of information. Particularly, learning to associate operations and roles with dynamic contexts is necessary for adapting user needs and making optimal classification to context-aware computing. However, a top-down based inference has difficulty to deal with various kinds of uncertainty such as unknown, ambiguous, imprecise, and erroneous related problems. Since misclassification by high-level classification to choice for learning the role acceptance may lead to wrong decision. Thus, we propose Evidential Fusion Network (EFN) that uses a belief or confidence level of information as a bottom-up based inference. In order to analyze advantage and disadvantage of the proposed EFN, we compare our approach (bottom-up) with decision tree approach (top-down). Finally, we suggest the combination of top-down based analysis and bottom-up based inference as a context reasoning method for obtaining reliable information.