Abstract
Context Pattern Method (CPM) is a statistical method that is used to quantify the validity of contextual information based on dependent contexts using previous knowledge about the system. The method exploits the interdependencies in a context aware system among entities and the environment in which they reside in order to calculate the Probability of Correctness (PoC) for a context under investigation. PoC expresses the level of confidence, that the contextual information sensed, are in fact correct or not. Obviously, each of the dependent contexts has a different importance to the context that is under investigation. Therefore its influence to the PoC measure needs to be weighted accordingly. In this paper we discuss the concept of feature weighting and show how feature selection algorithms can be applied for this purpose. We apply chi2, relief-f and mutual information, algorithms to the CPM method in order to weight the influence of the individual dependent contexts to the overall PoC measure and evaluate the method’s performance.
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