Experimental evaluation of two new GEP-based ensemble classifiers

The paper proposes applying Gene Expression Programming (GEP) to induce ensemble classifiers. Two new algorithms inducing such classifiers are proposed. The proposed ensemble classifiers use two different measures to select genes produced by the Gene Expression Programming procedure. Selection of genes from the set of the non-dominated ones in the process of meta-learning is supported by a genetic algorithm. Integration of genes (i.e. learners) is based on the majority voting. The proposed algorithms were validated experimentally using several datasets and the results were compared with those of other well established classification methods.

► Gene Expression Programming is used to define two new ensemble classifiers.
► Two quality measures are proposed and used in gene selection.
► Validation experiments were performed and results compared with other methods.
► Both classifiers give good classification accuracy.
► Both classifiers are competitive in terms of area under ROC curve.