Abstract
In this research work an ensemble of bagging, boosting, rotation forest, decorate and random subspace methods with 5 symbolic sub-classifiers in each one is presented. Then a voting methodology is used for the final prediction. In order to decrease training time, before building the ensemble redundant features were removed using a slight filter feature selection method. A comparison with simple bagging, boosting, rotation forest, decorate and random subspace methods ensembles with 25 symbolic sub-classifiers is performed, as well as other well-known combining methods, on standard benchmark datasets. The proposed technique is shown to be more accurate than other related methods in most cases.
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