Efficient Approximate Reasoning with Positive and Negative Information

Cornelis, Chris; Cock, Martine De; Kerre, Etienne

doi:10.1007/978-3-540-30133-2_102

Cited by 6 publications

(1 citation statement)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [33][34][35] we can trace the discussion revealing definitions of the implication generated by Kleene and Denies, Willmot, Mamdani and Assilian, Larsen, Gödel et al The trials of inserting individually created operations on fuzzy sets discern the approaches mentioned above. Even the item of compositional rule of inference was debated from separate points of views [36][37][38][39][40]. We can mention the Yager conception [40] and the Sugeno design [33] as the most original modifications of the initial version of the rule.…”

Section: Introductionmentioning

confidence: 99%

Computational Intelligence in Medical Decisions Making

Rakus-Andersson

Jain

2009

Studies in Computational Intelligence

View full text Add to dashboard Cite

Abstract. Computation intelligence paradigms including artificial neural networks, fuzzy systems, evolutionary computing techniques, intelligent agents and so on provide a basis for human like reasoning in medical systems. Approximate reasoning is one of the most effective fuzzy systems. The compositional rule of inference founded on the logical law modus ponens is furnished with a true conclusion, provided that the premises of the rule are true as well. Even though there exist different approaches to an implication, being the crucial part of the rule, we modify the early implication proposed in our practical model concerning a medical application. The approximate reasoning system presented in this work considers evaluation of a risk in the situation when physicians weigh necessity of the operation on a patient. The patient's clinical symptom levels, pathologically heightened, indicate the presence of a disease possible to recover by surgery. We wish to evaluate the extension of the operation danger by involving particularly designed fuzzy sets in the algorithm of approximate reasoning.

show abstract