Trujillo, Leonardo and Martínez, Yuliana and Galván-López, Edgar and Legrand, Pierrick (2011) Predicting problem difficulty for genetic programming applied to data classification. GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation. pp. 1355-1362.
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Abstract
During the development of applied systems, an important problem that must be addressed is that of choosing the correct tools for a given domain or scenario. This general task has been addressed by the genetic programming (GP) community by attempting to determine the intrinsic difficulty that a problem poses for a GP search. This paper presents an approach to predict the performance of GP applied to data classification, one of the most common problems in computer science. The novelty of the proposal is to extract statistical descriptors and complexity descriptors of the problem data, and from these estimate the expected performance of a GP classifier. We derive two types of predictive models: linear regression models and symbolic regression models evolved with GP. The experimental results show that both approaches provide good estimates of classifier performance, using synthetic and real-world problems for validation. In conclusion, this paper shows that it is possible to accurately predict the expected performance of a GP classifier using a set of descriptors that characterize the problem data.
| Item Type: | Article |
|---|---|
| Keywords: | Genetic Programming; Performance prediction; Classification; |
| Academic Unit: | Faculty of Science and Engineering > Computer Science Faculty of Science and Engineering > Research Institutes > Hamilton Institute |
| Item ID: | 15385 |
| Identification Number: | https://doi.org/10.1145/2001576.2001759 |
| Depositing User: | Edgar Galvan |
| Date Deposited: | 01 Feb 2022 12:46 |
| Journal or Publication Title: | GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation |
| Publisher: | Association for Computing Machinery |
| Refereed: | Yes |
| URI: | |
| Use Licence: | This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here |
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