A Theoretical Study on Six Classifier Fusion Strategies
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Dissimilarity representations allow for building good classifiers
Pattern Recognition Letters
An Experimental Comparison of Fixed and Trained Fusion Rules for Crisp Classifier Outputs
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
A Unified Framework for Subspace Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive classifier combination for visual information processing using data context-awareness
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
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 adaptive classifier combination scheme based on the cascade of classifier selection and fusion, called adaptive classifier combination scheme (ACCS). In the proposed scheme, system working environment is learned and the environmental context is identified. GA is used to search most effective classifier systems for each identified environmental context. The group of selected classifiers is combined based on GA model for reliable fusion. The knowledge of individual context and its associated chromo-somes 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. The proposed scheme has been tested in area of face recognition using standard FERET database, taking illumi-nation as an environmental context. Experimental result showed that using context awareness in classifier combination provides robustness to varying environmental conditions