@article {article, title = {Evolutionary Stratified Instance Selection applied to Training Set Selection for Extracting High Precise-Interpretable Classification Rules}, year = {2008}, month = {01}, author = {J. R. Cano and F. Herrera and Lozano, Manuel} } @article {610, title = {Making CN1 -SD Subgroup Discovery Algorithm Scalable to Large Size Data Sets Using Instance Selection}, journal = {Expert System with Applications}, volume = {35}, number = {4}, year = {2008}, pages = {1949-1965}, author = {J. R. Cano and F. Herrera and Lozano, Manuel and Garc{\'\i}a, Salvador} } @inbook {Cano2005, title = {Instance Selection Using Evolutionary Algorithms: An Experimental Study}, booktitle = {Advanced Techniques in Knowledge Discovery and Data Mining}, year = {2005}, pages = {127{\textendash}152}, publisher = {Springer London}, organization = {Springer London}, address = {London}, 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.}, isbn = {978-1-84628-183-9}, doi = {10.1007/1-84628-183-0_5}, url = {https://doi.org/10.1007/1-84628-183-0_5}, author = {J. R. Cano and F. Herrera and Lozano, Manuel}, editor = {Pal, Nikhil R. and Jain, Lakhmi} } @inbook {inbook, title = {Replacement Strategies to Maintain Useful Diversity in Steady-State Genetic Algorithms}, year = {2005}, month = {01}, pages = {85-96}, doi = {10.1007/3-540-32400-3_7}, author = {Lozano, Manuel and F. Herrera and J. R. Cano} } @inbook {Cano2005, title = {Strategies for Scaling Up Evolutionary Instance Reduction Algorithms for Data Mining}, booktitle = {Evolutionary Computation in Data Mining}, year = {2005}, pages = {21{\textendash}39}, publisher = {Springer Berlin Heidelberg}, organization = {Springer Berlin Heidelberg}, address = {Berlin, Heidelberg}, abstract = {Evolutionary algorithms are adaptive methods based on natural evolution that may be used for search and optimization. As instance selection can be viewed as a search problem, it could be solved using evolutionary algorithms.}, isbn = {978-3-540-32358-7}, doi = {10.1007/3-540-32358-9_2}, url = {https://doi.org/10.1007/3-540-32358-9_2}, author = {J. R. Cano and F. Herrera and Lozano, Manuel}, editor = {Ghosh, Ashish and Jain, Lakhmi C.} } @conference {739, title = {Selecci{\'o}n Evolutiva de Instancias en Miner{\'\i}a de Datos}, booktitle = {Workwhop de Miner{\'\i}a de Datos y Aprendizaje Autom{\'a}tico}, year = {2002}, month = {01}, address = {Santander (Espa{\~n}a)}, author = {J. R. Cano and F. Herrera and Lozano, Manuel} } @conference {inproceedings, title = {An evolutionary paradigm for designing fuzzy rule-based systems from examples}, year = {1997}, month = {10}, pages = {139 - 144}, isbn = {0-85296-693-8}, doi = {10.1049/cp:19971170}, author = {O. Cord{\'o}n and M. J. del Jesus and F. Herrera and Lozano, Manuel} }