DocumentCode
3126240
Title
Constraint Selection-Based Semi-supervised Feature Selection
Author
Hindawi, Mohammed ; Allab, Kais ; Benabdeslem, Khalid
Author_Institution
Univ. of Lyon, Villeurbanne, France
fYear
2011
fDate
11-14 Dec. 2011
Firstpage
1080
Lastpage
1085
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Data Mining (ICDM), 2011 IEEE 11th International Conference on
Conference_Location
Vancouver,BC
ISSN
1550-4786
Print_ISBN
978-1-4577-2075-8
Type
conf
DOI
10.1109/ICDM.2011.42
Filename
6137318
Link To Document