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Conference papers

Local-To-Global Semi-Supervised Feature selection

Mohammed Hindawi 1 Khalid Benabdeslem 1
1 DM2L - Data Mining and Machine Learning
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : Variable-weighting approaches are well-known in the context of embedded feature selection. Generally, feature selection is performed in a global way, when the algorithm selects a single cluster-independent subset of features (global feature selection). However, other approaches aim to select cluster-specific subsets of features (local feature selection). Global and local feature selection have different objectives, nevertheless, in this paper we propose a novel embedded approach which locally weighs the variables towards a global feature selection. The proposed approach is presented in the semi-supervised paradigm. Experiments on some known data sets are presented to validate our model and compare it with some representative methods.
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Submitted on : Wednesday, June 29, 2016 - 3:50:07 PM
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Mohammed Hindawi, Khalid Benabdeslem. Local-To-Global Semi-Supervised Feature selection. ACM International Conference on Information and Knowledge Management (CIKM 2013), Oct 2013, San Fransisco CA, United States. pp.2159-2168, ⟨10.1145/2505515.2505542⟩. ⟨hal-01339244⟩



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