Efficient semi-supervised feature selection: Constraint, Relevance and Redundancy.

Khalid Benabdeslem 1 Mohammed Hindawi 1
1 DM2L - Data Mining and Machine Learning
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : This paper describes a three-level framework for semi-supervised feature selection. Most feature selection methods mainly focus on finding relevant features for optimizing high-dimensional data. In this paper, we show that the relevance requires two important procedures to provide an efficient feature selection in the semi-supervised context. The first one concerns the selection of pairwise constraints that can be extracted from the labeled part of data. The second procedure aims to reduce the redundancy that could be detected in the selected relevant features. For the relevance, we develop a filter approach based on a constrained Laplacian score. Finally, experimental results are provided to show the efficiency of our proposal in comparison with several representative methods.
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Journal articles
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Submitted on : Monday, April 11, 2016 - 4:28:20 PM
Last modification on : Wednesday, November 20, 2019 - 3:11:44 AM

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Khalid Benabdeslem, Mohammed Hindawi. Efficient semi-supervised feature selection: Constraint, Relevance and Redundancy.. IEEE Transactions on Knowledge and Data Engineering, Institute of Electrical and Electronics Engineers, 2014, 5, 26, pp.1131-1143. ⟨10.1109/TKDE.2013.86⟩. ⟨hal-01301033⟩

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