Principal component neural networks: theory and applications
Principal component neural networks: theory and applications
Evolutionary Pursuit and Its Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Theoretical Study on Six Classifier Fusion Strategies
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reconfigurable Context-Sensitive Middleware for Pervasive Computing
IEEE Pervasive Computing
Designing classifier fusion systems by genetic algorithms
IEEE Transactions on Evolutionary Computation
Switching between selection and fusion in combining classifiers: anexperiment
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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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.