A methodology for the design of experiments in computational intelligence with multiple regression models.

ABSTRACT: The design of experiments and the validation of the results achieved with them are vital in any research study. This paper focuses on the use of different Machine Learning approaches for regression tasks in the field of Computational Intelligence and especially on a correct comparison between the different results provided for different methods, as those techniques are complex systems that require further study to be fully understood. A methodology commonly accepted in Computational intelligence is implemented in an R package called RRegrs. This package includes ten simple and complex regression models to carry out predictive modeling using Machine Learning and well-known regression algorithms. The framework for experimental design presented herein is evaluated and validated against RRegrs

SUBMITTER: Fernandez-Lozano C 

PROVIDER: S-EPMC5136129 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature