Re: Fuzziness: what am I missing?

Anthony Cowden (cowden@sonalysts.com)
Mon, 6 Apr 1998 04:11:25 +0200 (MET DST)

> >
> > >I don't know about others, but I see FL as an extremely useful technique
> > >for capturing ordinal information from language. Things that we
> > >previously talked about and intuitively "knew" can be captured in a
> > >mathematical model and included in a computer programme. E.g., "The
> > >robber was tall (short, fat, thin, dark-skinned, young, old, quick,
> > >slow, dim-witted, etc.)." FL provides techniques to put this into a
> > >database and do comparisons: e.g., "From our list of know felons, who
> > >best matches the above description." Incredible yet so incredibly easy!
> > >Not that there isn't debate about the details (especially here where
> > >we're mixing FL with decision theory).
> > >Keeping the faith,
> > >Michael D. Kersey
> >
> > Can you please give any references and/or examples
> > of solving problems mentioned above?
> >
> > I need to do some comparisons of entities described
> > in a similar way (skin diseases to be exact).
> >
> > My solution uses scoring. Perhaps other approach could
> > be better?
> > Which person resembles a fat, old robber better:
> > young fat or old slim suspect?
> Well, I think it was "young fat", but I'm not really sure!-))
> >
> > Evaluating the importance of individual features is necessary,
> > and I cannot get it working right. (but there are problems
> > with information avilable, with different importance of
> > the features depending on the setting etc.)
> >
> > Another problem is incomplete descriptions of diagnostic
> > units (= not only the description of a suspect, but the
> > felony database is incomplete).
> >
> > Common fuzzy comparators seem to underestimate
> > or neglect secondary, less important signs.
> >
> > I would appreciate any literary pointer to similar
> > problem solution.
> >

"Fuzzy Logic for Business and Industry:, by Earl Cox, pp. 212-215. A
Criminal (Suspect) Identification System.

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