Variable precision rough set model
Journal of Computer and System Sciences
Communications of the ACM
Machine Learning
On the Accuracy of Meta-learning for Scalable Data Mining
Journal of Intelligent Information Systems
Classification by pairwise coupling
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Data mining: concepts and techniques
Data mining: concepts and techniques
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Machine Learning
Using Correspondence Analysis to Combine Classifiers
Machine Learning
Experiments on Solving Multiclass Learning Problems by n2-classifier
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Ensembles of Learning Machines
WIRN VIETRI 2002 Proceedings of the 13th Italian Workshop on Neural Nets-Revised Papers
Boolean Reasoning for Decision Rules Generation
ISMIS '93 Proceedings of the 7th International Symposium on Methodologies for Intelligent Systems
Handbook of data mining and knowledge discovery
Handbook of data mining and knowledge discovery
Fundamenta Informaticae
On Using Rule Induction in Multiple Classifiers with a Combiner Aggregation Strategy
ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
RIONA: A New Classification System Combining Rule Induction and Instance-Based Learning
Fundamenta Informaticae
Rough set approach to sunspot classification problem
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
Ensembles of Abstaining Classifiers Based on Rule Sets
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
Argument based generalization of MODLEM rule induction algorithm
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
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
Experienced physicians and automatic generation of decision rules from clinical data
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Transactions on rough sets XII
Rough set-based analysis of characteristic features for ANN classifier
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
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
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Problems of using elements of rough sets theory and rule induction to create efficient classifiers are discussed. In the last decade many researches attempted to increase a classification accuracy by combining several classifiers into integrated systems. The main aim of this paper is to summarize the author's own experience with applying one of his rule induction algorithm, called MODLEM, in the framework of different combined classifiers, namely, the bagging, n2-classifier and the combiner aggregation. We also discuss how rough approximations are applied in rule induction. The results of carried out experiments have shown that the MODLEM algorithm can be efficiently used within the framework of considered combined classifiers.