C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
A simple, fast, and effective rule learner
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Classification trees for problems with monotonicity constraints
ACM SIGKDD Explorations Newsletter
A Simple Approach to Ordinal Classification
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Variable Consistency Model of Dominance-Based Rough Sets Approach
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
An Algorithm for Induction of Decision Rules Consistent with the Dominance Principle
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Inference for the Generalization Error
Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Developing and Testing Models for Replicating Credit Ratings: A Multicriteria Approach
Computational Economics
Internet content filtering using isotonic separation on content category ratings
ACM Transactions on Internet Technology (TOIT)
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Maximum likelihood rule ensembles
Proceedings of the 25th international conference on Machine learning
Stochastic dominance-based rough set model for ordinal classification
Information Sciences: an International Journal
Statistical Model for Rough Set Approach to Multicriteria Classification
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Ensemble of decision rules for ordinal classification with monotonicity constraints
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Large-Margin thresholded ensembles for ordinal regression: theory and practice
ALT'06 Proceedings of the 17th international conference on Algorithmic Learning Theory
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
| Hi-index | 0.00 |
Ordinal classification problems with monotonicity constraints (also referred to as multicriteria classification problems) often appear in real-life applications, however, they are considered relatively less frequently in theoretical studies than regular classification problems. We introduce a rule induction algorithm based on the statistical learning approach that is tailored for this type of problems. The algorithm first monotonizes the dataset (excludes strongly inconsistent objects), using Stochastic Dominance-based Rough Set Approach, and then uses forward stagewise additive modeling framework for generating a monotone rule ensemble. Experimental results indicate that taking into account knowledge about order andmonotonicity constraints in the classifier can improve the prediction accuracy.