  Recent work proposed the computation of so-called PI-explanations of Naive
Bayes Classifiers (NBCs). PI-explanations are subset-minimal sets of
feature-value pairs that are sufficient for the prediction, and have been
computed with state-of-the-art exact algorithms that are worst-case exponential
in time and space. In contrast, we show that the computation of one
PI-explanation for an NBC can be achieved in log-linear time, and that the same
result also applies to the more general class of linear classifiers.
Furthermore, we show that the enumeration of PI-explanations can be obtained
with polynomial delay. Experimental results demonstrate the performance gains
of the new algorithms when compared with earlier work. The experimental results
also investigate ways to measure the quality of heuristic explanations
