Evolution of classification rules for comprehensible knowledge discovery

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.

• Type: Conference
• Year: 2007
• Where: CEC
• Authors: Emiliano Carreno, Guillermo Leguizamón, Neal Wagner
• Keywords: Artificial Intelligence, CEC 2007, Data Mining Framework, Rule Evolution, Rule Induction Algorithm