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Rule set learning has recently been frequently revisited because of itsinterpretability. Existing methods have several shortcomings though. First,most existing methods impose orders among rules, either explicitly orimplicitly, which makes the models less comprehensible. Second, due to thedifficulty of handling conflicts caused by overlaps (i.e., instances covered bymultiple rules), existing methods often do not consider probabilistic rules.Third, learning classification rules for multi-class target is understudied, asmost existing methods focus on binary classification or multi-classclassification via the ``one-versus-rest" approach.  To address these shortcomings, we propose TURS, for Truly Unordered RuleSets. To resolve conflicts caused by overlapping rules, we propose a novelmodel that exploits the probabilistic properties of our rule sets, with theintuition of only allowing rules to overlap if they have similar probabilisticoutputs. We next formalize the problem of learning a TURS model based on theMDL principle and develop a carefully designed heuristic algorithm. Webenchmark against a wide range of rule-based methods and demonstrate that ourmethod learns rule sets that have lower model complexity and highly competitivepredictive performance. In addition, we empirically show that rules in ourmodel are empirically ``independent" and hence truly unordered.

Probabilistic Truly Unordered Rule Sets