Ordinal Classification with Monotonicity Constraints by Variable Consistency Bagging

Abstract

We propose an ensemble method that solves ordinal classification problem with monotonicity constraints. The classification data is structured using the Variable Consistency Dominance-based Rough Set Approach (VC-DRSA). The method employs a variable consistency bagging scheme to produce bootstrap samples that privilege objects (i.e., classification examples) with relatively high values of consistency measures used in VC-DRSA. In result, one obtains an ensemble of rule classifiers learned on bootstrap samples. Due to diversification of bootstrap samples controlled by consistency measures, the ensemble of classifiers gets more accurate, which has been acknowledged by a computational experiment on benchmark data.