Strengthening the Forward Variable Selection Stopping Criterion
(2009)
Presentation / Conference Contribution
Herrera, L. J., Rubio, G., Pomares, H., Paechter, B., Guillén, A., & Rojas, I. (2009). Strengthening the Forward Variable Selection Stopping Criterion. In Artificial Neural Networks – ICANN 2009 (215-224). https://doi.org/10.1007/978-3-642-04277-5_22
Given any modeling problem, variable selection is a preprocess step that selects the most relevant variables with respect to the output variable. Forward selection is the most straightforward strategy for variable selection; its application using the... Read More about Strengthening the Forward Variable Selection Stopping Criterion.