  Characteristic rules have been advocated for their ability to improve
interpretability over discriminative rules within the area of rule learning.
However, the former type of rule has not yet been used by techniques for
explaining predictions. A novel explanation technique, called CEGA
(Characteristic Explanatory General Association rules), is proposed, which
employs association rule mining to aggregate multiple explanations generated by
any standard local explanation technique into a set of characteristic rules. An
empirical investigation is presented, in which CEGA is compared to two
state-of-the-art methods, Anchors and GLocalX, for producing local and
aggregated explanations in the form of discriminative rules. The results
suggest that the proposed approach provides a better trade-off between fidelity
and complexity compared to the two state-of-the-art approaches; CEGA and
Anchors significantly outperform GLocalX with respect to fidelity, while CEGA
and GLocalX significantly outperform Anchors with respect to the number of
generated rules. The effect of changing the format of the explanations of CEGA
to discriminative rules and using LIME and SHAP as local explanation techniques
instead of Anchors are also investigated. The results show that the
characteristic explanatory rules still compete favorably with rules in the
standard discriminative format. The results also indicate that using CEGA in
combination with either SHAP or Anchors consistently leads to a higher fidelity
compared to using LIME as the local explanation technique.
