  This study proposed an exhaustive stable/reproducible rule-mining algorithm
combined to a classifier to generate both accurate and interpretable models.
Our method first extracts rules (i.e., a conjunction of conditions about the
values of a small number of input features) with our exhaustive rule-mining
algorithm, then constructs a new feature space based on the most relevant rules
called "local features" and finally, builds a local predictive model by
training a standard classifier on the new local feature space. This local
feature space is easy interpretable by providing a human-understandable
explanation under the explicit form of rules. Furthermore, our local predictive
approach is as powerful as global classical ones like logistic regression (LR),
support vector machine (SVM) and rules based methods like random forest (RF)
and gradient boosted tree (GBT).
