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An introduction to variable and feature selection (2003)

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by Isabelle Guyon
Venue:Journal of Machine Learning Research
Citations:431 - 8 self
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BibTeX

@ARTICLE{Guyon03anintroduction,
    author = {Isabelle Guyon},
    title = {An introduction to variable and feature selection},
    journal = {Journal of Machine Learning Research},
    year = {2003},
    volume = {3},
    pages = {1157--1182}
}

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Abstract

Variable and feature selection have become the focus of much research in areas of application for which datasets with tens or hundreds of thousands of variables are available.

Citations

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1485 Pattern Classification - Duda, Hart, et al. - 2001
936 A training algorithm for optimal margin classifiers - Boser, Guyon, et al. - 1992
541 G: Significance analysis of microarrays applied to the ionizing radiation response - VG, Tibshirani, et al.
477 Distributional clustering of English words - Pereira, Tishby, et al. - 1993
474 Gene selection for cancer classification using support vector machines - Guyon, Weston, et al. - 2002
417 Approximate statistical tests for comparing supervised classification learning algorithms - Dietterich - 1998
375 Optimum brain damage - Cun, Denker, et al. - 1990
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328 The information bottleneck method - Tishby, Pereira, et al. - 1999
301 Toward optimal feature selection - Koller, Sahami - 1996
277 LA: A practical approach to feature selection - Kira, Rendell - 1992
268 Support vector machine classification and validation of cancer tissue samples using microarray expression data - Furey, Cristianini, et al. - 2000
264 Classification and Regression - Breiman, Friedman, et al. - 1984
180 An Extensive Empirical Study of Feature Selection Metrics for Text Classification - Forman
163 Feature Selection for SVMs - Weston, Mukherjee, et al. - 2000
147 al.Molecular classification of cancer -class discovery and class prediction by gene expression monitoring .Science,286(5439):531-537(1999 - Golub
115 Inference for the Generalization Error - Bengio, Nadeau - 1999
85 Use of the zero norm with linear models and kernel methods - Weston, Elisseff, et al.
82 A divisive information-theoretic feature clustering algorithm for text classification - Dhillon, Mallela, et al.
60 Feature extraction by non parametric mutual information maximization - Torkkola - 2003
54 On the approximability of minimizing nonzero variables or unsatisfied relations in linear systems - Amaldi, Kann - 1998
51 Grafting: Fast, incremental feature selection by gradient descent in function space - Perkins, Lacker, et al.
51 Variable selection using svm-based criteria - Rakotomamonjy
48 Distributional word clusters vs. words for text categorization - Bekkerman, El-Yaniv, et al. - 2003
45 Dimensionality reduction via sparse support vector machines - Bi, Bennett, et al.
37 A new metric-based approach to model selection - Schuurmans - 1997
36 GH: Wrappers for feature selection - Kohavi, John - 1997
35 Feature selection and dualities in maximum entropy discrimination - Jebara, Jaakkola - 2000
34 Adaptive scaling for feature selection in svms - Grandvalet, Canu - 2002
34 Overfitting in making comparisons between variable selection methods - Reunanen
32 On feature selection: Learning with exponentially many irrelevant features as training examples - Ng - 1998
28 Ranking a random feature for variable and feature selection - STOPPIGLIA, DREYFUS, et al.
28 Estimation of dependences based on empirical data. Springer Series in Statistics - Vapnik - 1998
27 Sufficient dimensionality reduction - Globerson, Tishby - 2003
24 The elements of statistical learning. Springer Series in Statistics - Hastie, Tibshirani, et al. - 2001
19 Regression Selection and Shrinkage Via the Lasso - Tibshirani - 1995
11 Extensions to Metric-Based Model Selection - Bengio, Chapados - 2003
11 Convergence rates of the voting gibbs classifier, with application to bayesianfeature selection - Ng, Jordan - 2001
9 Personnaz L: MLPs (Mono-Layer Polynomials and Multi-Layer Perceptrons) for nonlinear modeling - Rivals
8 G: Withdrawing an example from the training set: an analytic estimation of its effect on a nonlinear parameterized model. Neurocomputing 2000, 35(1-4):195-201. et al - Monari, Dreyfus
7 Benefitting from the variables that variable selection discards - Caruana, Sa - 2003
5 Bayesian Input Variable Selection Using Posterior Probabilities and Expected Utilities,” Technical Report, Helsinki University of Technology, Laboratory of Computational Engineering Publications. ISSN 1457-1404, 2002. Mohammed Awad received the BSc degree - Vehtari, Lampinen
2 Distributional word clusters vs - Bekkerman, El-Yaniv, et al. - 2003
1 An Introduction to Variable and Feature Selection - Furey, Cristianini, et al.
1 words for text categorization. Journal of Machine Learning Research, 3:1183--1208 (this issue - Ben-Hur, Guyon - 2003
1 An Introduction to Variable and Feature Selection T. R. Golub et al. Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring - Forman - 1999
1 Extensions to metric-based model selection. JMLR, 3:1209–1227 (this issue - Bengio, Chapados - 2003
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