|Title||Instance Selection Using Evolutionary Algorithms: An Experimental Study|
|Publication Type||Book Chapter|
|Year of Publication||2005|
|Authors||Cano, J. R., Herrera F., and Lozano Manuel|
|Editor||Pal, Nikhil R., and Jain Lakhmi|
|Book Title||Advanced Techniques in Knowledge Discovery and Data Mining|
In this chapter, we carry out an empirical study of the performance of four representative evolutionary algorithm models considering two instance-selection perspectives, the prototype selection and the training set selection for data reduction in knowledge discovery. This study includes a comparison between these algorithms and other nonevolutionary instance-selection algorithms. The results show that the evolutionary instance-selection algorithms consistently outperform the nonevolutionary ones, offering two main advantages simultaneously, better instance-reduction rates and higher classification accuracy.