- author = "Emiliano Carreno and Guillermo Leguizamon and Neal Wagner",
- title = "Evolution of Classification Rules for Comprehensible Knowledge Discovery",
- booktitle = "2007 IEEE Congress on Evolutionary Computation",
- year = "2007",
- editor = "Dipti Srinivasan and Lipo Wang",
- pages = "1261--1268",
- address = "Singapore",
- month = "25-28 " # sep,
- organization = "IEEE Computational Intelligence Society",
- publisher = "IEEE Press",
- ISBN = "1-4244-1340-0",
- file = "1695.pdf",
- keywords = "genetic algorithms, genetic programming",
-
DOI = "
doi:10.1109/CEC.2007.4424615",
- 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.",
-
notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and
the IET.
IEEE Catalog Number: 07TH8963C",
