Rule-Mining based classification: a benchmark study
This work addresses the need for interpretable machine learning models, but it is incremental as it builds on existing rule-mining and classification techniques.
The study tackled the problem of creating interpretable classification models by proposing a rule-mining based method that combines exhaustive rule extraction with a classifier, achieving performance comparable to standard methods like logistic regression and random forest.
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).