Evolutionary stratified training set selection for extracting classification rules with trade off precision-interpretability
By José Ramón Cano, Francisco Herrera, Manuel Lozano. 2007
109 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Information Systems
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Francisco Herrera
- Alberto Fernández
- Salvador Garcia
- Sebastián Ventura
- Pedro G. Espejo
- Julián Luengo
- Enrique Herrera-Viedma
- M. J. Jesús
In Information Systems
- Francisco Herrera
- Salvador Garcia
- Pedro G. Espejo
- Alberto Fernández
- Sebastián Ventura
- Cristóbal Romero
- Ben Kai Daniel
Citations in the corpus
- Genetic fuzzy systems: taxonomy, current research trends and prospects (2008, methodology): “For example, it has been used for extracting decision trees, see [13, 92].”
- Enhancing the effectiveness and interpretability of decision tree and rule induction classifiers with evolutionary training set selection over imbalanced problems (2009, methodology): “As the evolutionary computation method, we have used the CHC model [15,7].”
- A Survey on the Application of Genetic Programming to Classification (2010, background): “A similar situation arises with instance selection, where a subset of the training set is selected in order to speed up the learning process, without compromising classification rates [17].”