Title :
Constraint Selection-Based Semi-supervised Feature Selection
Author :
Hindawi, Mohammed ; Allab, Kais ; Benabdeslem, Khalid
Author_Institution :
Univ. of Lyon, Villeurbanne, France
Abstract :
In this paper, we present a novel feature selection approach based on an efficient selection of pair wise constraints. This aims at selecting the most coherent constraints extracted from labeled part of data. The relevance of features is then evaluated according to their efficient locality preserving and chosen constraint preserving ability. Finally, experimental results are provided for validating our proposal with respect to other known feature selection methods.
Keywords :
constraint handling; data handling; constraint preservation; constraint selection based semisupervised feature selection; locality preservation; pairwise constraints; Accuracy; Clustering algorithms; Coherence; Data mining; Feature extraction; Laplace equations; Vectors; Dimensionality reduction; constraint selection; feature selection;
Conference_Titel :
Data Mining (ICDM), 2011 IEEE 11th International Conference on
Conference_Location :
Vancouver,BC
Print_ISBN :
978-1-4577-2075-8
DOI :
10.1109/ICDM.2011.42
