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2010
IEEE

Feature selection is the ReliefF for multiple instance learning

8 years 7 months ago
Feature selection is the ReliefF for multiple instance learning
Dimensionality reduction and feature selection in particular are known to be of a great help for making supervised learning more effective and efficient. Many different feature selection techniques have been proposed for the traditional settings, where each instance is expected to have a label. In multiple instance learning (MIL) each example or bag consists of a variable set of instances, and the label is known for the bag as a whole, but not for the individual instances it consists of. Therefore, utilizing class labels for feature selection in MIL is not that straightforward and traditional approaches for feature selection are not directly applicable. This paper proposes a filter feature selection approach based on the ReliefF technique. It allows any previously designed MIL method to benefit from our feature selection approach, which helps to cope with the curse of dimensionality. Experimental results show the effectiveness of the proposed approach in MIL
Amelia Zafra, Mykola Pechenizkiy, Sebastián
Added 13 Feb 2011
Updated 13 Feb 2011
Type Journal
Year 2010
Where ISDA
Authors Amelia Zafra, Mykola Pechenizkiy, Sebastián Ventura
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