Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.21/5081
Título: Efficient feature selection filters for high-dimensional data
Autor: Ferreira, Artur J.
Figueiredo, Mário A. T.
Palavras-chave: Feature Selection
Dispersion Measures
Similarity Measures
High-Dimensional Data
Sparse Logistic-Regression
Feature Subset-Selection
Floating Search Methods
Multiple Data Sets
Gene Selection
Statistical Comparisons
Bound Algorithm
Data: 1-Out-2012
Editora: Elsevier Science BV
Citação: FERREIRA, Artur J.; FIGUEIREDO, Mário A. T. – Efficient feature selection filters for high-dimensional data. Pattern Recognition Letters. ISSN: 0167-8655. Vol. 33, nr. 13 (2012), pp. 1794-1804
Resumo: Feature selection is a central problem in machine learning and pattern recognition. On large datasets (in terms of dimension and/or number of instances), using search-based or wrapper techniques can be cornputationally prohibitive. Moreover, many filter methods based on relevance/redundancy assessment also take a prohibitively long time on high-dimensional. datasets. In this paper, we propose efficient unsupervised and supervised feature selection/ranking filters for high-dimensional datasets. These methods use low-complexity relevance and redundancy criteria, applicable to supervised, semi-supervised, and unsupervised learning, being able to act as pre-processors for computationally intensive methods to focus their attention on smaller subsets of promising features. The experimental results, with up to 10(5) features, show the time efficiency of our methods, with lower generalization error than state-of-the-art techniques, while being dramatically simpler and faster.
Peer review: yes
URI: http://hdl.handle.net/10400.21/5081
DOI: 10.1016/j.patrec.2012.05.019
ISSN: 0167-8655
Aparece nas colecções:ISEL - Eng. Elect. Tel. Comp. - Artigos

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