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 decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
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
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Classification trees for problems with monotonicity constraints
ACM SIGKDD Explorations Newsletter
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Developing and Testing Models for Replicating Credit Ratings: A Multicriteria Approach
Computational Economics
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Rule learning with monotonicity constraints
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Learning Rule Ensembles for Ordinal Classification with Monotonicity Constraints
Fundamenta Informaticae - Fundamentals of Knowledge Technology
Rough Set Approach to Knowledge Discovery about Preferences
ICCCI '09 Proceedings of the 1st International Conference on Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems
Large-margin feature selection for monotonic classification
Knowledge-Based Systems
Learning Rule Ensembles for Ordinal Classification with Monotonicity Constraints
Fundamenta Informaticae - Fundamentals of Knowledge Technology
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Ordinal classification problemswithmonotonicity 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 forward stagewise additive modeling that is tailored for this type of problems. The algorithm monotonizes the dataset (excludes highly inconsistent objects) using Dominance-based Rough Set Approach and generates monotone rules. Experimental results indicate that taking into account the knowledge about order and monotonicity constraints in the classifier can improve the prediction accuracy.