Abstract
In this paper, we present the application of a Multi-Agent Classifier System (MACS) to medical data classification tasks. The MACS model comprises a number of Fuzzy Min–Max (FMM) neural network classifiers as its agents. A trust measurement method is used to integrate the predictions from multiple agents, in order to improve the overall performance of the MACS model. An auction procedure based on the sealed bid is adopted for the MACS model in determining the winning agent. The effectiveness of the MACS model is evaluated using the Wisconsin Breast Cancer (WBC) benchmark problem and a real-world heart disease diagnosis problem. The results demonstrate that stable results are produced by the MACS model in undertaking medical data classification tasks.
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Acknowledgments
The authors gratefully acknowledge funding from the Fundamental Research Grant Scheme (No. PELECT/203/6711229) for supporting this project.
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Mohammed, M.F., Lim, C.P., bt Ngah, U.K. (2014). Applying a Multi-Agent Classifier System with a Novel Trust Measurement Method to Classifying Medical Data. In: Mat Sakim, H., Mustaffa, M. (eds) The 8th International Conference on Robotic, Vision, Signal Processing & Power Applications. Lecture Notes in Electrical Engineering, vol 291. Springer, Singapore. https://doi.org/10.1007/978-981-4585-42-2_41
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DOI: https://doi.org/10.1007/978-981-4585-42-2_41
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