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Generating redundant features with unsupervised multi-tree genetic programming

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conference contribution
posted on 16.06.2020 by Andrew Lensen, Bing Xue, Mengjie Zhang
© Springer International Publishing AG, part of Springer Nature 2018. Recently, feature selection has become an increasingly important area of research due to the surge in high-dimensional datasets in all areas of modern life. A plethora of feature selection algorithms have been proposed, but it is difficult to truly analyse the quality of a given algorithm. Ideally, an algorithm would be evaluated by measuring how well it removes known bad features. Acquiring datasets with such features is inherently difficult, and so a common technique is to add synthetic bad features to an existing dataset. While adding noisy features is an easy task, it is very difficult to automatically add complex, redundant features. This work proposes one of the first approaches to generating redundant features, using a novel genetic programming approach. Initial experiments show that our proposed method can automatically create difficult, redundant features which have the potential to be used for creating high-quality feature selection benchmark datasets.

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Preferred citation

Lensen, A., Xue, B. & Zhang, M. (2018, January). Generating redundant features with unsupervised multi-tree genetic programming. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (10781 LNCSpp. 84-100). Springer International Publishing. https://doi.org/10.1007/978-3-319-77553-1_6

Title of proceedings

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume

10781 LNCS

Publication or Presentation Year

01/01/2018

Pagination

84-100

Publisher

Springer International Publishing

Publication status

Published

ISSN

0302-9743

eISSN

1611-3349

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