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Learning using Unselected Features (LUFe)
conference contribution
posted on 2023-06-09, 01:28 authored by Joseph G Taylor, Viktoriia SharmanskaViktoriia Sharmanska, Kristian Kersting, David WeirDavid Weir, Novi QuadriantoNovi QuadriantoFeature 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.
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Publication status
- Published
File Version
- Published version
Journal
Proceedings of the 25th International Joint Conference on Artificial Intelligence; New York; 9–15 July 2016Publisher
AAAI Press / International Joint Conferences on Artificial IntelligencePublisher URL
Page range
2060-2066Book title
Proceedings of the twenty-fifth international joint conference on artificial intelligenceISBN
9781577357711Department affiliated with
- Informatics Publications
Full text available
- Yes
Peer reviewed?
- Yes
Editors
Subbarao KambhampatiLegacy Posted Date
2016-06-03First Open Access (FOA) Date
2016-09-23First Compliant Deposit (FCD) Date
2016-06-03Usage metrics
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