C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Rough set approach to incomplete information systems
Information Sciences: an International Journal
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
International Journal of Human-Computer Studies
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
An Implementation of Logical Analysis of Data
IEEE Transactions on Knowledge and Data Engineering
Machine Learning
A Comparison of Several Approaches to Missing Attribute Values in Data Mining
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Incomplete data and generalization of indiscernibility relation, definability, and approximations
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Solving Regression by Learning an Ensemble of Decision Rules
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
Relationship Between Loss Functions and Confirmation Measures
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Ordinal classification with decision rules
MCD'07 Proceedings of the 3rd ECML/PKDD international conference on Mining complex data
Hi-index | 0.00 |
In this paper, we consider an algorithm that generates an ensemble of decision rules. A single rule is treated as a specific subsidiary, base classifier in the ensemble that indicates only one of the decision classes. Experimental results have shown that the ensemble of decision rules is as efficient as other machine learning methods. In this paper we concentrate on a common problem appearing in real-life data that is a presence of missing attributes values. To deal with this problem, we experimented with different approaches inspired by rough set approach to knowledge discovery. Results of those experiments are presented and discussed in the paper.