Information Technology Reference
In-Depth Information
The difference and similarities among fuzzy measure and fuzziness in measure
are presented, demonstrating how important these approaches assume in decision,
diagnosis, analysis, assessment, classification, therapeutic conduct. Conditional re-
strictions represented as fuzzy sets are elastic restrictions associated to the possibil-
ity that an evidence can occur. It assumes, thus, a key role in the reasoning that is
neither exact nor inexact. The role of approximate reasoning is then emphasized and
the manner it is able to capture the subjectivity, vagueness, and inexact information
is also described. The computational rule of inference is demonstrated to assume a
fundamental mechanism not only to obtain both fuzzy and singleton inferred values
but to understand the different stages that fuzziness is present.
Finally, the fuzzy inference system is advocated as a mechanism that allows mim-
icking the human reasoning to deal with environments (systems) that are complex,
imperfect, and approximate. Such an approach is an alternative to substitute human
beings in the task of modifying or helping in deciding how to modify systems to
obtain safer, more effective, more efficient, higher quality, and lower costs. In par-
ticular, this technique has been investigated to help or to substitute professionals in
diverse areas of medicine or health care, eliminating human mistakes due to human
fails, tiring etc. In so doing, the fuzzy decision support system is an alternative for
reducing the error in determining the therapeutic conduct.
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