Case-Based Reasoning: Experiences, Lessons and Future Directions
Case-Based Reasoning: Experiences, Lessons and Future Directions
IEEE Intelligent Systems
Learning adaptation knowledge to improve case-based reasoning
Artificial Intelligence
The role of data warehousing in bioterrorism surveillance
Decision Support Systems
Mining competent case bases for case-based reasoning
Artificial Intelligence
Is there a need for fuzzy logic?
Information Sciences: an International Journal
Fuzzy Systems Engineering: Toward Human-Centric Computing
Fuzzy Systems Engineering: Toward Human-Centric Computing
Finite cut-based approximation of fuzzy sets and its evolutionary optimization
Fuzzy Sets and Systems
Shadowed sets: representing and processing fuzzy sets
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Investigating a relevance of fuzzy mappings
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
KASER: knowledge amplification by structured expert randomization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Linguistic models as a framework of user-centric system modeling
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Owing to their inherent nature, terrorist activities could be highly diversified. The risk assessment becomes a crucial component as it helps us weigh pros and cons versus possible actions or some planning pursuits. The recognition of threats and their relevance/seriousness is an integral part of the overall process of classification, recognition, and assessing eventual actions undertaken in presence of acts of chem.-bio terrorism. In this study, we introduce an overall scheme of risk assessment realized on a basis of classification results produced for some experimental data capturing the history of previous threat cases. The structural relationships in these experimental data are first revealed with the help of information granulation - fuzzy clustering. We introduce two criteria using which information granules are evaluated, that is (a) representation capabilities which are concerned with the quality of representation of numeric data by abstract constructs such as information granules, and (b) interpretation aspects which are essential in the process of risk evaluation. In case of representation facet of information granules, we demonstrate how a reconstruction criterion quantifies their quality. Three ways in which interpretability is enhanced are studied. First, we show how to construct the information granules with extended cores (where the uncertainty associated with risk evaluation could be reduced) and shadowed sets, which provide a three-valued logic perspective of information granules given in the form of fuzzy sets. Subsequently, we show a way of interpreting fuzzy sets via an optimized set of its @a-cuts.