C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
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
Discovering data mining: from concept to implementation
Discovering data mining: from concept to implementation
Instance Selection and Construction for Data Mining
Instance Selection and Construction for Data Mining
Adaptive Sampling Methods for Scaling Up Knowledge Discovery Algorithms
Data Mining and Knowledge Discovery
Combining Classifiers with Meta Decision Trees
Machine Learning
Distributed learning with bagging-like performance
Pattern Recognition Letters
Rule Induction with CN2: Some Recent Improvements
EWSL '91 Proceedings of the European Working Session on Machine Learning
Ensembles of Learning Machines
WIRN VIETRI 2002 Proceedings of the 13th Italian Workshop on Neural Nets-Revised Papers
Empirical comparisons of various voting methods in bagging
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Learning Ensembles from Bites: A Scalable and Accurate Approach
The Journal of Machine Learning Research
Selective costing ensemble for handling imbalanced data sets
International Journal of Hybrid Intelligent Systems
Recursive data mining for role identification in electronic communications
International Journal of Hybrid Intelligent Systems
Hybrid classifiers based on semantic data subspaces for two-level text categorization
International Journal of Hybrid Intelligent Systems
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Learning algorithms are an integral part of the Data Mining (DM) process. However, DM deals with a large amount of data and most learning algorithms do not operate in massive datasets. A technique often used to ease this problem is related to data sampling and the construction of ensembles of classifiers. Several methods to construct such ensembles have been proposed. However, these methods often lack an explanation facility. This work proposes methods to construct ensembles of symbolic classifiers. These ensembles can be further explored in order to explain their decisions to the user. These methods were implemented in the ELE system, also described in this work. Experimental results in two out of three datasets show improvement over all base-classifiers. Moreover, according to the obtained results, methods based on single rule classification might be used to improve the explanation facility of ensembles.