Standardization and Data Augmentation in Genetic Programming

About this item

Title

Standardization and Data Augmentation in Genetic Programming

Content partner

University of Otago

Collection

Otago University Research Archive

Description

Genetic programming (GP) is a common method for performing symbolic regression that relies on the use of ephemeral random constants in order to adequately scale predictions. Suitable values for these constants must be drawn from appropriate, but typically unknown, distributions for the problem being modeled. While rarely used with GP, Z -score standardization of feature and response spaces often significantly improves the predictive performance of GP by removing scale issues and reducing erro...

Read more

Format

Research paper

Research format

Scholarly text / Journal article

Thesis level

Article

Date created

2022-12

Creator

Owen, Caitlin A. / Dick, Grant / Whigham, Peter A.

URL

https://hdl.handle.net/10523/24959

Related subjects

Bias-variance decomposition / data augmentation / Data models / evolutionary machine learning / feature scaling / genetic programming (GP) / Mathematical models / prediction error / Predictive models / Sensitivity / standardization / Standards / symbolic regression / Training / Training data

What is the copyright status of this item?

All Rights Reserved

This item is all rights reserved, which means you'll have to get permission from University of Otago before using it.

What can I do with this item?

You must always check with University of Otago to confirm the specific terms of use, but this is our understanding:

Non-infringing use

NZ Copyright law does not prevent every use of a copyright work. You should consider what you can and cannot do with a copyright work.

No sharing

You may not copy and/or share this item with others without further permission. This includes posting it on your blog, using it in a presentation, or any other public use.

No modifying

You are not allowed to adapt or remix this item into any other works.

No commercial use

You may not use this item commercially.