RULEM: rule learning with monotonicity constraints for ordinal classification

In many real world applications classification models are required to be in line with domain knowledge and to respect monotone relations between predictor variables and the target class, in order to be acceptable for implementation. This paper presents a novel algorithm, called RULEM, to induce monotone ordinal rule based classification models. The proposed approach can be applied in combination with any rule- or treebased classification technique, since monotonicity is guaranteed during a postprocessing step. The algorithm checks whether a rule set or decision tree violates the imposed monotonicity constraints, and existing violations are resolved by inducing a set of additional rules which enforce monotone classification. The algorithm is able to handle non-monotonic noise, and can be applied to both partially and totally monotone problems with an ordinal target variable. Using the RULEM concept, two novel justifiability measures are introduced which allow to calculate the extent to which a classification model is in line with domain knowledge expressed in the form of monotonicity constraints. An extensive benchmarking experiment indicates that RULEM preserves the predictive power of the rule induction technique while guaranteeing monotone classification.status: accepte