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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