On Sun, 23 Feb 2014 15:49:12 -0800, "JoeCL" <firstname.lastname@example.org> wrote:
>Hello Everyone, > >I'm working with artificial neural networks and I need to derive some >indication of how well the networks are identifying intended targets versus >non-targets. > >The thing is that all targets are definitely not created equal. Some targets >are much more important than others to get right, while others are of lower >importance.
I'm wondering whether you are confounding "importance" with frequency ... where you might consider the rare ones important, OR you might consider the common ones important.
> >I looked at this problem from a coin-toss perspective and used an online >binomial calculator >(http://stattrek.com/online-calculator/binomial.aspx#TopPage) to try to say >that the chances of the neural network identifying targets by dumb luck was >exceedingly unlikely. But this is based on all targets being of equal value >and assuming a simple correctness measure (fraction right out of the total >targets the network thought it found).
You might want to look at something like "Measures of association for cross classifications" by Goodman and Kruskal. JASA, 1954, or textbook, 1979.
For symmetrical measures of success or effect, the Odds Ratio has the advantage of being very transparent. If you do have data for which it is important not to miss certain outcomes, you may prefer some asymmetrical measure. However, if you want a test on a single outcome, almost every test comes very close to the result of the simple contingency chisquared test on the 2x2 table (Yes/No) by (Predicted/Actual).
If you have a large number of targets, I think you must have a pretty good idea that you will have *some* success -- Thus, a marginal test of "significance", I think, should not be very interesting.
Again, you should be looking for *measures* of success, not *tests* of success.
> >If, as I said, the targets are of unequal value, then a correctness measure >is only part of what's needed and some kind of weighting is involved. > >Do you have any thoughts about how to derive statistics that demonstrate the >statistical significance of a neural network's "correctness" versus sheer >chance when the targets are of unequal importance? And then, how would I >state the results?
(Repeating myself....) I think you are probably off-base when you try to force the question into the shape of "statistical significance", especially if you are concerned with matching some criterion ("p < 0.01"). Neural networks - in my small base of information - are poorly suited for direct tests because they look at far too many possibilities. And that should be the case, even more so, when there are "multiple targets."
For a test, you need to measure the success of cross-validation, not of the immediate fit. Find the measures that tell you something interesting, Odds ratio or otherwise. Is it enough to look at the separate targets, or do you need something overall? Are you satisfied by selecting out a subset of targets, in order to reflect their extra importance?
If your fit is pretty good, you might focus on the count of actual *errors* (or "important errors"?) instead of some more complicated statistic.