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Learning using Unselected Features (LUFe)

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conference contribution
posted on 2023-06-09, 01:28 authored by Joseph G Taylor, Viktoriia SharmanskaViktoriia Sharmanska, Kristian Kersting, David WeirDavid Weir, Novi QuadriantoNovi Quadrianto
Feature selection has been studied in machine learning and data mining for many years, and is a valuable way to improve classification accuracy while reducing model complexity. Two main classes of feature selection methods - filter and wrapper - discard those features which are not selected, and do not consider them in the predictive model. We propose that these unselected features may instead be used as an additional source of information at train time. We describe a strategy called Learning using Unselected Features (LUFe) that allows selected and unselected features to serve different functions in classification. In this framework, selected features are used directly to set the decision boundary, and unselected features are utilised in a secondary role, with no additional cost at test time. Our empirical results on 49 textual datasets show that LUFe can improve classification performance in comparison with standard wrapper and filter feature selection.

History

Publication status

  • Published

File Version

  • Published version

Journal

Proceedings of the 25th International Joint Conference on Artificial Intelligence; New York; 9–15 July 2016

Publisher

AAAI Press / International Joint Conferences on Artificial Intelligence

Page range

2060-2066

Book title

Proceedings of the twenty-fifth international joint conference on artificial intelligence

ISBN

9781577357711

Department affiliated with

  • Informatics Publications

Full text available

  • Yes

Peer reviewed?

  • Yes

Editors

Subbarao Kambhampati

Legacy Posted Date

2016-06-03

First Open Access (FOA) Date

2016-09-23

First Compliant Deposit (FCD) Date

2016-06-03

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