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 |
Pagination | 127–152 |
Publisher | Springer London |
City | London |
ISBN Number | 978-1-84628-183-9 |
Abstract | 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. |
URL | https://doi.org/10.1007/1-84628-183-0_5 |
DOI | 10.1007/1-84628-183-0_5 |