C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Using and combining predictors that specialize
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
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
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Towards tight bounds for rule learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Optimizing abstaining classifiers using ROC analysis
ICML '05 Proceedings of the 22nd international conference on Machine learning
Evidence Contrary to the Statistical View of Boosting
The Journal of Machine Learning Research
Machine Learning and Data Mining: Introduction to Principles and Algorithms
Machine Learning and Data Mining: Introduction to Principles and Algorithms
On combined classifiers, rule induction and rough sets
Transactions on rough sets VI
Integrating selective pre-processing of imbalanced data with Ivotes ensemble
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
A comparison of three voting methods for bagging with the MLEM2 algorithm
IDEAL'10 Proceedings of the 11th international conference on Intelligent data engineering and automated learning
Modifications of classification strategies in rule set based bagging for imbalanced data
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
IIvotes ensemble for imbalanced data
Intelligent Data Analysis - Combined Learning Methods and Mining Complex Data
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The role of abstaining from prediction by component classifiers in rule ensembles is discussed. We consider bagging and Ivotes approaches to construct such ensembles. In our proposal, component classifiers are based on unordered sets of rules with a classification strategy that solves ambiguous matching of the object's description to the rules. We propose to induce rule sets by a sequential covering algorithm and to apply classification strategies using either rule support or discrimination measures. We adopt the classification strategies to abstaining by not using partial matching. Another contribution of this paper is an experimental evaluation of the effect of the abstaining on performance of ensembles. Results of comprehensive comparative experiments show that abstaining rule sets classifiers improve the accuracy, however this effect is more visible for bagging than for Ivotes.