A theoretical characterization of linear SVM-based feature selection

By D. Hardin, Ioannis Tsamardinos, Constantin F. Aliferis. 2004

67 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Computer Vision and Pattern Recognition

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Citations in the corpus

Interpreting support vector machine models for multivariate group wise analysis in neuroimaging (2015, positive): “…for interpreting SVM models Rasmussen et al. (2011) Guyon et al. (2002), it has been noted that using SVM weights can assign relatively low weights to significant features and relatively larger weights to irrelevant features Hardin et al. (2004) Cuingnet et al. (2011) Gaonkar and Davatzikos (2013).”
A multiple kernel support vector machine scheme for feature selection and rule extraction from gene expression data of cancer tissue (2007, background): “also points out that in gene expression data even random gene subsets give good classification performance [32].”
Reproducing Kernel Hilbert Spaces Associated with Analytic Translation-Invariant Mercer Kernels (2008, background): “In some problems of feature selection [ 6 , 10] and manifold learning [2], data of derivatives may be available [9, 18].”
A Survey of Text Classification Algorithms (2012, methodology): “Therefore, a number of techniques were proposed in [134] in order to learn the associations between the words and the categories.”
Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part I: Algorithms and Empirical Evaluation (2010, methodology): “…to the above results we would add the arguments in favor of causal feature selection presented in Guyon et al. (2007) and recent theoretical (Hardin et al., 2004) and empirical (Statnikov et al., 2006) results that show that under the same sufficient conditions that make Markov blanket the…”
A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis (2004, unknown): “On the other hand, the SVM algorithm may assign non-zero weights to weakly relevant variables [ Hardin2004 ] which explains why effective variable selection can still improve SVM classification.”