FIRE: An Optimization Approach for Fast Interpretable Rule Extraction
This work addresses the need for fast and interpretable rule extraction for practitioners in machine learning, offering an incremental improvement over existing methods.
The authors tackled the problem of extracting interpretable decision rules from tree ensembles by developing FIRE, an optimization-based framework that selects sparse, representative rule subsets and encourages rule fusion for enhanced interpretability. The result was a specialized solver that performed up to 40x faster than existing methods and outperformed state-of-the-art algorithms in building sparse, interpretable rule sets.
We present FIRE, Fast Interpretable Rule Extraction, an optimization-based framework to extract a small but useful collection of decision rules from tree ensembles. FIRE selects sparse representative subsets of rules from tree ensembles, that are easy for a practitioner to examine. To further enhance the interpretability of the extracted model, FIRE encourages fusing rules during selection, so that many of the selected decision rules share common antecedents. The optimization framework utilizes a fusion regularization penalty to accomplish this, along with a non-convex sparsity-inducing penalty to aggressively select rules. Optimization problems in FIRE pose a challenge to off-the-shelf solvers due to problem scale and the non-convexity of the penalties. To address this, making use of problem-structure, we develop a specialized solver based on block coordinate descent principles; our solver performs up to 40x faster than existing solvers. We show in our experiments that FIRE outperforms state-of-the-art rule ensemble algorithms at building sparse rule sets, and can deliver more interpretable models compared to existing methods.