We present the Multi-value Rule Set (MRS) for interpretable classification with feature efficient presentations. Compared to rule sets built from single-value rules, MRS adopts a more generalized form of association rules that allows multiple values in a condition. Rules of this form are more concise than classical single-value rules in capturing and describing patterns in data. Our formulation also pursues a higher efficiency of feature utilization, which reduces possible cost in data collection and storage. We propose a Bayesian framework for formulating an MRS model and develop an efficient inference method for learning a maximum a posteriori, incorporating theoretically grounded bounds to iteratively reduce the search space and improve the search efficiency. Experiments on synthetic and real-world data demonstrate that MRS models have significantly smaller complexity and fewer features than baseline models while being competitive in predictive accuracy.

The paper proposes learning sets of decision rules that can express the disjunction of feature values in atoms of the rules, for example, IF color yellow OR red, THEN stop. The emphasis is on interpretability, and the paper argues that these multi-value rules are more interpretable than similarly trained decision sets that do not support multi-value rules. Following prior work, the paper proposes placing a prior distribution over the parameters of the decision set, such as the number of rules and the maximum number of atoms in each rule. The paper derives bounds on the resulting distribution to accelerate a simulated annealing learning algorithm. Experiments show that multi-value rule sets are as accurate as other classifiers proposed as interpretable model classes, such as Bayesian rule sets on benchmark decision problems.

We present the Multi-value Rule Set (MRS) for interpretable classification with feature efficient presentations. Compared to rule sets built from single-value rules, MRS adopts a more generalized form of association rules that allows multiple values in a condition. Rules of this form are more concise than classical single-value rules in capturing and describing patterns in data. Our formulation also pursues a higher efficiency of feature utilization, which reduces possible cost in data collection and storage. We propose a Bayesian framework for formulating an MRS model and develop an efficient inference method for learning a maximum a posteriori, incorporating theoretically grounded bounds to iteratively reduce the search space and improve the search efficiency.