Context Awareness System Modeling and Classifier Combination

Abstract

This paper proposes a novel classifier combination system that can be used by classification systems under dynamically varying environments. The proposed method adopts the concept of context-awareness and the similarity between classes, and the system working environments are learned (clustered) and identified as environmental contexts. The proposed method fitness correlation table is used to explore the most effective classifier combination for each identified context. We use t-test for classifier selection and fusion decision and proposed context modeling and t-test. The group of selected classifiers is combined based on t-test decision model for reliable fusion. The knowledge of individual context and its associated chromosomes representing the optimal classifier combination is stored in the context knowledge base. Once the context knowledge is accumulated the system can react to dynamic environment in real time.