English

Rectifying Mono-Label Boolean Classifiers

Artificial Intelligence 2022-09-07 v2

Abstract

We elaborate on the notion of rectification of a Boolean classifier Σ\Sigma. Given Σ\Sigma and some background knowledge TT, postulates characterizing the way Σ\Sigma must be changed into a new classifier ΣT\Sigma \star T that complies with TT have already been presented. We focus here on the specific case of mono-label Boolean classifiers, i.e., there is a single target concept and any instance is classified either as positive (an element of the concept), or as negative (an element of the complementary concept). In this specific case, our main contribution is twofold: (1) we show that there is a unique rectification operator \star satisfying the postulates, and (2) when Σ\Sigma and TT are Boolean circuits, we show how a classification circuit equivalent to ΣT\Sigma \star T can be computed in time linear in the size of Σ\Sigma and TT; when Σ\Sigma and TT are decision trees, a decision tree equivalent to ΣT\Sigma \star T can be computed in time polynomial in the size of Σ\Sigma and TT.

Cite

@article{arxiv.2206.08758,
  title  = {Rectifying Mono-Label Boolean Classifiers},
  author = {Sylvie Coste-Marquis and Pierre Marquis},
  journal= {arXiv preprint arXiv:2206.08758},
  year   = {2022}
}

Comments

8 pages, rewriting of motivations in the Introduction section and of Example 3 and Example 4 explanations, typo corrected in Example 4 and captions of Figure 4 and Figure 5 rectified

R2 v1 2026-06-24T11:55:04.183Z