Evolving classifier ensembles with voting predictors

In XCS with computed prediction, namely XCSF, the classifier prediction parameter is replaced by a parametrized prediction function. So far, the works on the computed prediction in XCSF has been limited to evolve a single type of prediction function at once. Recently, several works studied and extended the computed prediction in XCSF. However, it is still not clear how the most adequate prediction function should be chosen for a given problem. In this paper we introduce XCSF with voting predictors that extends XCSF to let it select best prediction function to use in each problem subspace. We compared XCSFV to XCSF on several problems. Our results suggest that XCSFV performs as well as XCSF with the best prediction function in all the tested problems. In addition, XCSFV finds the most accurate prediction function in each problem subspace.