Learning and classification of monotonic ordinal concepts
Computational Intelligence
Machine Learning
Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Rule learning with monotonicity constraints
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Monotonic Variable Consistency Rough Set Approaches
International Journal of Approximate Reasoning
Sequential covering rule induction algorithm for variable consistency rough set approaches
Information Sciences: an International Journal
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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.