Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.21/5074
Título: An unsupervised approach to feature discretization and selection
Autor: Ferreira, Artur J.
Figueiredo, Mário A. T.
Palavras-chave: Feature Discretization
Feature Quantization
Feature Selection
Linde-Buzo-Gray Algorithm
Sparse Data
Support Vector Machines
Naive Bayes
K-Nearest Neighbor
Random Subspace Method
Microarray Data
Gene Selection
Classification
Algorithm
Cancer
Information
Redundancy
Relevance
Data: Set-2012
Editora: Elsevier SCI LTD
Citação: FERREIRA, Artur J.; FIGUEIREDO, Mário A. T. – An unsupervised approach to feature discretization and selection. Pattern Recognition. ISSN: 0031-3203. Vol 45, nr. 9 (2012), pp. 3048-3060
Resumo: Many learning problems require handling high dimensional datasets with a relatively small number of instances. Learning algorithms are thus confronted with the curse of dimensionality, and need to address it in order to be effective. Examples of these types of data include the bag-of-words representation in text classification problems and gene expression data for tumor detection/classification. Usually, among the high number of features characterizing the instances, many may be irrelevant (or even detrimental) for the learning tasks. It is thus clear that there is a need for adequate techniques for feature representation, reduction, and selection, to improve both the classification accuracy and the memory requirements. In this paper, we propose combined unsupervised feature discretization and feature selection techniques, suitable for medium and high-dimensional datasets. The experimental results on several standard datasets, with both sparse and dense features, show the efficiency of the proposed techniques as well as improvements over previous related techniques.
Peer review: yes
URI: http://hdl.handle.net/10400.21/5074
DOI: 10.1016/j.patcog.2011.12.008
ISSN: 0031-3203
Aparece nas colecções:ISEL - Eng. Elect. Tel. Comp. - Artigos



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