Learning using Unselected Features (LUFe)

Taylor, Joseph G, Sharmanska, Viktoriia, Kersting, Kristian, Weir, David and Quadrianto, Novi (2016) Learning using Unselected Features (LUFe). Published in: Kambhampati, Subbarao, (ed.) Proceedings of the 25th International Joint Conference on Artificial Intelligence; New York; 9–15 July 2016. 2060-2066. AAAI Press / International Joint Conferences on Artificial Intelligence ISBN 9781577357711

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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.

Item Type: Conference Proceedings
Schools and Departments: School of Engineering and Informatics > Informatics
Subjects: Q Science > QA Mathematics > QA0276 Mathematical statistics
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Depositing User: Novi Quadrianto
Date Deposited: 03 Jun 2016 12:28
Last Modified: 19 Jun 2017 09:25
URI: http://srodev.sussex.ac.uk/id/eprint/61286

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