A first analysis of the effect of local and global optimization weights methods in the cooperative-competitive design of RBFN for imbalanced environments

Many real applications are composed of data sets where the distribution of the classes is significantly different. These data sets are commonly known as imbalanced data sets. Proposed approaches that address this problem can be categorized into two types: data-based, which resample problem data in a preprocessing phase and algorithm-based which modify or create new methods to address the imbalance problem. In this paper, CO2 RBFN a cooperative-competitive design method for Radial Basis Function Networks that has previously demonstrated a good behaviour tackling imbalanced data sets, is tested using two different training weights algorithms, local and global, in order to gain knowledge about this problem. As conclusions we can outline that a more global optimizer training algorithm obtains worse results.