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• W2481466205 abstract "In this study, the fuzzy causal map inference mechanisms are analyzed for decision making tasks and a comparative analysis is performed to handle with the uncertainty in the problem of pulmonary risk prediction. Fuzzy Cognitive Mapping (FCM) is a causal graphical representation including nodes, determining the most relevant factors of a complex system, and links between these nodes determining the relationships between those factors. It represents knowledge in a symbolic manner and relates states, processes, policies, events, values, and inputs in an analogous manner. In the proposed work, a modified inference mechanism for FCM approach, which handles uncertainty and missing data, is presented and compared with the common fuzzy causal graph reasoning process for a medical diagnosis problem. Through this study, we overcome the problem of missing data and/or incomplete knowledge, especially for the cases where there is no any information about a concept-state or the knowledge of some concepts is insufficient. By this way, the rescaled inference process is proved more reliable, yielding more exact and natural inference results than traditional FCMs. A number of different scenarios for medical diagnosis concentrated on the pulmonary infections are elaborated to demonstrate the functioning of the rescaled FCM inference mechanism." @default.
• W2481466205 created "2016-08-23" @default.
• W2481466205 creator A5037965915 @default.
• W2481466205 date "2012-01-01" @default.
• W2481466205 modified "2023-09-27" @default.
• W2481466205 title "Fuzzy Cognitive Map Reasoning Mechanism for Handling Uncertainty and Missing Data" @default.
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• W2481466205 doi "https://doi.org/10.4018/978-1-61350-429-1.ch029" @default.
• W2481466205 hasPublicationYear "2012" @default.
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