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Learning Using Unselected Features (LUFe)

Joseph G. Taylor; Viktoriia Sharmanska; Kristian Kersting; David Weir; Novi Quadrianto
In: Subbarao Kambhampati (Hrsg.). Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. International Joint Conference on Artificial Intelligence (IJCAI-2016), July 9-15, New York, NY, USA, Pages 2060-2066, IJCAI/AAAI Press, 2016.


Feature selection has been studied in machine learning and data mining for many years, and is a valuable way to improve classification accu- racy while reducing model complexity. Two main classes of feature selection methods - filter and wrapper - discard those features which are not se- lected, and do not consider them in the predictive model. We propose that these unselected features may instead be used as an additional source of in- formation 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 frame- work, 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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