A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Artificial Intelligence Review - Special issue on lazy learning
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Modeling Context Information in Pervasive Computing Systems
Pervasive '02 Proceedings of the First International Conference on Pervasive Computing
Maintaining Continuous Dependability in Sensor-Based Context-Aware Pervasive Computing Systems
HICSS '05 Proceedings of the Proceedings of the 38th Annual Hawaii International Conference on System Sciences - Volume 09
Human-Computer Interaction
Measuring the Probability of Correctness of Contextual Information in Context Aware Systems
DASC '09 Proceedings of the 2009 Eighth IEEE International Conference on Dependable, Autonomic and Secure Computing
Validating Context Information in Context Aware Systems
IE '10 Proceedings of the 2010 Sixth International Conference on Intelligent Environments
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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.