A proposal of evolutionary prototype selection for class imbalance problems

Unbalanced data in a classification problem appears when there are many more instances of some classes than others. Several solutions were proposed to solve this problem at data level by under-sampling. The aim of this work is to propose evolutionary prototype selection algorithms that tackle the problem of unbalanced data by using a new fitness function. The results obtained show that a balancing of data performed by evolutionary under-sampling outperforms previously proposed under-sampling methods in classification accuracy, obtaining reduced subsets and getting a good balance on data.