Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Combination of Multiple Classifiers Using Adaptive Fuzzy Integral
ICAIS '02 Proceedings of the 2002 IEEE International Conference on Artificial Intelligence Systems (ICAIS'02)
Proceedings of the 2007 conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies
Object Recognition by Permanence of Ratios Based Fusion and Gaussian Bayes Decision
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part I
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We present a fuzzy fusion approach for combining cell-phase identification results obtained from multiple classifiers. This approach can improve the classification rates and allows the task of high-content cell-cycle screening more effective for biomedical research in the study of structures and functions of cells and molecules. Conventionally such study requires the processing and analysis of huge amounts of image data, and manual image analysis is very time consuming, thus costly, and also potentially inaccurate and poorly reproducible. The proposed method has been used to combine the results from three classifiers, and the combined result is superior to any of the results obtained from a single classifier.