Comparing Genetic Programming with Other Data Mining Techniques on Prediction Models

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Azimlu:2019:ICCSE,

• author = "Fateme Azimlu and Shahryar Rahnamayan and Masoud Makrehchi and Naveen Kalra",
• booktitle = "2019 14th International Conference on Computer Science Education (ICCSE)",
• title = "Comparing Genetic Programming with Other Data Mining Techniques on Prediction Models",
• year = "2019",
• pages = "785--791",
• month = aug,
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/ICCSE.2019.8845381",
• ISSN = "2473-9464",
• abstract = "Prediction is one of the most important tasks in the machine learning field. Data scientists employ various learning methods to find the most appropriate and accurate model for each family of applications or dataset. This study compares the symbolic regression using genetic programming (GP), with conventional machine learning techniques. In cases it is required to model an unknown, poorly understood, and/or complicated system. In these cases, we use genetic programming to generate a symbolic model without using any pre-known model. In this paper, the GP is studied as a tool for prediction in different types of datasets and conducted experiments to verify the superiority of GP over conventional models in certain conditions and datasets. The accuracy of GP-based regression results are compared with other machine learning techniques, and are found to be more accurate in certain conditions.",
• notes = "Also known as \cite{8845381}",

}

Genetic Programming entries for Fateme Azimlu Shahryar Rahnamayan Masoud Makrehchi Naveen Kalra

Citations