Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Ensemble learning for free with evolutionary algorithms?
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Selective ensemble of decision trees
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
Inducing oblique decision trees with evolutionary algorithms
IEEE Transactions on Evolutionary Computation
GP ensembles for large-scale data classification
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
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Classifier ensemble techniques are effectively used to combine the responses provided by a set of classifiers. Classifier ensembles improve the performance of single classifier systems, even if a large number of classifiers is often required. This implies large memory requirements and slow speeds of classification, making their use critical in some applications. This problem can be reduced by selecting a fraction of the classifiers from the original ensemble. In this work, it is presented an ensemble-based framework that copes with large datasets, however selecting a small number of classifiers composing the ensemble. The framework is based on two modules: an ensemble-based Genetic Programming (GP) system, which produces a high performing ensemble of decision tree classifiers, and a Bayesian Network (BN) approach to perform classifier selection. The proposed system exploits the advantages provided by both techniques and allows to strongly reduce the number of classifiers in the ensemble. Experimental results compare the system with well-known techniques both in the field of GP and BN and show the effectiveness of the devised approach. In addition, a comparison with a pareto optimal strategy of pruning has been performed.