Decision Rule-based Algorithm for Ordinal Classification based on Rank Loss Minimization

Many classification problems have in fact an ordinal nature, i.e., the class labels are ordered. We introduce a decision rule algorithm, called RankRules, tailored for this type of problems, that is based on minimization of the rank loss. In general, the complexity of the rank loss minimization is quadratic with respect to the number of training examples, however, we show that the introduced algorithm works in linear time (plus sorting time of attribute values that is performed once in the pre-processing phase). The rules are built using a boosting approach. The impurity measure used for building single rules is derived using one of four minimization techniques often encountered in boosting. We analyze these techniques focusing on the trade-off between misclassification and coverage of the rule. RankRules is verified in the computational experiment showing its competitiveness to other algorithms.