hi! Your work sounds like mine-----finding under causal structures from statistical data. It like bayesian network learning. waiting for more discuss.
- --- Enric Hernandez <[EMAIL PROTECTED]> wrote: > Hello, > > I have developed (well, adapted some old ones would > be more precise) > some algorithms for my Ph.D.intended to extract > if-then rules from a > supervised set of data in uncertain environments > (assuming > <attribute,value> paradigm for the description of > data). > > I am mainly interested in obtaining rules which > express the underlying > structure of data more than in getting highly > accurate classification > rules (of course, both approaches are not mutually > exclusive. Instead, > both are > usually correlated: the better the set of rules > "reflect" the > underlying structure, the better they perform on > classification > tasks).In order to do that, I am using artificial > dataset generators, > where you can specify a set of "seed" rules which > will generate the > dataset. > > My purpose is testing how good the extracted rules > resemble the > original set of "seed" rules. As far as I know, this > is not the common > approach which usually performs statistical tests > aimed to test > cassification accuracy. > > Does anyone of you have any hint on that? Any idea > or reference will > be of great help to me. > > Thank you very much and kind regards! > > Enric Hernandez > Universitat Politecnica de Catalunya. > Barcelona. (Spain) > > > P.D: apologies if my question falls out of the scope > of this group >
