%0 Conference Paper %1 Carreno:2007:cec %A Carreno, Emiliano %A Leguizamon, Guillermo %A Wagner, Neal %B 2007 IEEE Congress on Evolutionary Computation %C Singapore %D 2007 %E Srinivasan, Dipti %E Wang, Lipo %I IEEE Press %K algorithms, genetic programming %P 1261--1268 %T Evolution of Classification Rules for Comprehensible Knowledge Discovery %X This article, which lies within the data mining framework, proposes a method to build classifiers based on the evolution of rules. The method, named REC (Rule Evolution for Classifiers), has three main features: it applies genetic programming to perform a search in the space of potential solutions; a procedure allows biasing the search towards regions of comprehensible hypothesis with high predictive quality and it includes a strategy for the selection of an optimum subset of rules (classifier) from the rules obtained as the result of the evolutionary process. A comparative study between this method and the rule induction algorithm C5.0 is carried out for two application problems (data sets). Experimental results show the advantages of using the method proposed. %@ 1-4244-1340-0

@inproceedings{Carreno:2007:cec, abstract = {This article, which lies within the data mining framework, proposes a method to build classifiers based on the evolution of rules. The method, named REC (Rule Evolution for Classifiers), has three main features: it applies genetic programming to perform a search in the space of potential solutions; a procedure allows biasing the search towards regions of comprehensible hypothesis with high predictive quality and it includes a strategy for the selection of an optimum subset of rules (classifier) from the rules obtained as the result of the evolutionary process. A comparative study between this method and the rule induction algorithm C5.0 is carried out for two application problems (data sets). Experimental results show the advantages of using the method proposed.}, added-at = {2008-06-19T17:35:00.000+0200}, address = {Singapore}, author = {Carreno, Emiliano and Leguizamon, Guillermo and Wagner, Neal}, biburl = {https://www.bibsonomy.org/bibtex/26bb29ba5162ff24ac367316fae1a1650/brazovayeye}, booktitle = {2007 IEEE Congress on Evolutionary Computation}, editor = {Srinivasan, Dipti and Wang, Lipo}, file = {1695.pdf}, interhash = {4d2d1ad432513aa8814f80e4bb8ea56e}, intrahash = {6bb29ba5162ff24ac367316fae1a1650}, isbn = {1-4244-1340-0}, keywords = {algorithms, genetic programming}, month = {25-28 September}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, organization = {IEEE Computational Intelligence Society}, pages = {1261--1268}, publisher = {IEEE Press}, timestamp = {2008-06-19T17:37:23.000+0200}, title = {Evolution of Classification Rules for Comprehensible Knowledge Discovery}, year = 2007 }