RBF Networks Ensemble Construction based on Evolutionary Multi-objective Optimization

The ensemble learning has attracted much attention over the last decade. While constructing RBF network ensemble the generally encountered problems are how to construct diverse RBF networks and how to combine outputs. The construction of RBF network can be considered as a multi-objective optimization problem regarding model complexity. A set of RBF networks which is multi-objectively optimized can be obtained by solving the above-mentioned problem. In this paper the construction of RBF networks by evolutionary multi-objective optimization method and its ensemble are considered, and it is applied to the pattern classification problem. Also some ensemble member selection methods and output combination methods are considered. Experimental study on the benchmark problem of pattern classification is carried out; then it is illustrated that the RBF network ensemble has a performance, which is comparable to that of other ensemble methods.