  How to interpret a data mining model has received much attention recently,
because people may distrust a black-box predictive model if they do not
understand how the model works. Hence, it will be trustworthy if a model can
provide transparent illustrations on how to make the decision. Although many
rule-based interpretable classification algorithms have been proposed, all
these existing solutions cannot directly construct an interpretable model to
provide personalized prediction for each individual test sample. In this paper,
we make a first step towards formally introducing personalized interpretable
classification as a new data mining problem to the literature. In addition to
the problem formulation on this new issue, we present a greedy algorithm called
PIC (Personalized Interpretable Classifier) to identify a personalized rule for
each individual test sample. To demonstrate the necessity, feasibility and
advantages of such a personalized interpretable classification method, we
conduct a series of empirical studies on real data sets. The experimental
results show that: (1) The new problem formulation enables us to find
interesting rules for test samples that may be missed by existing
non-personalized classifiers. (2) Our algorithm can achieve the same-level
predictive accuracy as those state-of-the-art (SOTA) interpretable classifiers.
(3) On a real data set for predicting breast cancer metastasis, such a
personalized interpretable classifier can outperform SOTA methods in terms of
both accuracy and interpretability.
