La Trobe

File(s) under embargo

Reason: Publisher conditions apply

217

days

22

hours

until file(s) become available

Genetic programming for multiple-feature construction on high-dimensional classification

journal contribution
posted on 10.12.2020, 02:32 by Binh Tran, Bing Xue, Mengjie Zhang
© 2019 Elsevier Ltd

Data representation is an important factor in deciding the performance of machine learning algorithms including classification. Feature construction (FC)can combine original features to form high-level ones that can help classification algorithms achieve better performance. Genetic programming (GP)has shown promise in FC due to its flexible representation. Most GP methods construct a single feature, which may not scale well to high-dimensional data. This paper aims at investigating different approaches to constructing multiple features and analysing their effectiveness, efficiency, and underlying behaviours to reveal the insight of multiple-feature construction using GP on high-dimensional data. The results show that multiple-feature construction achieves significantly better performance than single-feature construction. In multiple-feature construction, using multi-tree GP representation is shown to be more effective than using the single-tree GP thanks to the ability to consider the interaction of the newly constructed features during the construction process. Class-dependent constructed features achieve better performance than the class-independent ones. A visualisation of the constructed features also demonstrates the interpretability of the GP-based FC approach, which is important to many real-world applications.

Funding

This work was supported in part by the Marsden Fund of New Zealand Government under Contracts VUW1509 and VUW1615, Huawei Industry Fund E2880/3663, and the University Research Fund at Victoria University of Wellington 209862/3580, and 213150/3662.

History

Publication Date

01/09/2019

Journal

Pattern Recognition

Volume

93

Pagination

14p. (p. 404-417)

Publisher

Elsevier

ISSN

0031-3203

Rights Statement

The Author reserves all moral rights over the deposited text and must be credited if any re-use occurs. Documents deposited in OPAL are the Open Access versions of outputs published elsewhere. Changes resulting from the publishing process may therefore not be reflected in this document. The final published version may be obtained via the publisher’s DOI. Please note that additional copyright and access restrictions may apply to the published version.

Exports