  Error-correcting codes (ECC) are used to reduce multiclass classification
tasks to multiple binary classification subproblems. In ECC, classes are
represented by the rows of a binary matrix, corresponding to codewords in a
codebook. Codebooks are commonly either predefined or problem dependent. Given
predefined codebooks, codeword-to-class assignments are traditionally
overlooked, and codewords are implicitly assigned to classes arbitrarily. Our
paper shows that these assignments play a major role in the performance of ECC.
Specifically, we examine similarity-preserving assignments, where similar
codewords are assigned to similar classes. Addressing a controversy in existing
literature, our extensive experiments confirm that similarity-preserving
assignments induce easier subproblems and are superior to other assignment
policies in terms of their generalization performance. We find that
similarity-preserving assignments make predefined codebooks become
problem-dependent, without altering other favorable codebook properties.
Finally, we show that our findings can improve predefined codebooks dedicated
to extreme classification.
