Hi Karol,
Those would indeed be nice additions. However, we should do benchmarks on
real data and focus on the most effective methods.
I found this paper / software which could serve as a reference:
http://jmlr.org/papers/v11/escalera10a.html
Mathieu
On Mon, Aug 12, 2013 at 1:27 PM, Karol Pysniak <kpysn...@gmail.com> wrote:
> Hi All,
>
> Currently, scikit-learn uses randomly generated codebook for
> error-correcting output-code (line 468 in sklearn/multiclass.py). However,
> there are some interesting strategies we could use in sklearn. In
> particular, I would like to start from trying:
>
> 1. BCH Codes as mentioned in section 2.3.4
> http://www.cs.cmu.edu/afs/cs/project/jair/pub/volume2/dietterich95a.pdf
>
> 2. Decriminant ECOC as presented in
> O. Pujol, P. Radeva, , and J. Vitria`. Discriminant ECOC: A heuristic
> method for application dependent design of error correcting output codes
>
> Also, for now we use only euclidean distance to find the nearest class as
> represented in a codebook. We could add some new, for example, Humming
> distance.
>
> What do you think about those new enhancements?
>
> Thanks,
> Karol
>
>
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