Gene Selection for Cancer Classification using Support Vector Machines

By Isabelle M Guyon, Jason Aaron Edward Weston, S. Barnhill, Vladimir Naumovich Vapnik. 2002

6,066 citations recorded by the source corpus.

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Citations in the corpus

Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers (2013, background): “Other authors propose SVM-based systems for privacy-critical tasks, such as cancer diagnostic [30, 7], text categorization [36], or face recognition [4].”
Adversarial Feature Selection Against Evasion Attacks (2016, methodology): “Selecting a relevant subset of features may indeed not only improve classiﬁer generalization, but it may also signiﬁcantly reduce computational complexity and allow for a better data understanding [37], [38].”
A Benchmark for Interpretability Methods in Deep Neural Networks (2018, methodology): “Recursive feature elimination methods [16] are a greedy search where the algorithm is trained on an iteratively altered subset of features.”
Feature Selection via Dependence Maximization (2012, methodology): “8 Recursive Feature Elimination with Support Vectors Another popular feature selection algorithm is to use Support Vector Machines and to determine the relevance of features by the size of the induced margin as a solution of the dual optimization problem (Guyon et al., 2002).”
Supervised feature selection via dependence estimation (2007, background): “Forward selection tries to increase Q ( T ) as much as possible for each inclusion of features, and backward elimination tries to achieve this for each deletion of features (Guyon et al., 2002).”
A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis (2004, neutral): “In clinical bioinformatics, they have allowed the construction of powerful experimental cancer diagnostic models based on gene expression data with thousands of variables and as little as few dozen samples (e.g. Furey et al., 2000; Guyon et al., 2002; Aliferis et al., 2003a).”
Selecting critical features for data classification based on machine learning methods (2020, methodology): “Guyon et al. [74] pro - posed RFE, which is applied to cancer classification by using SVM. RFE employs all features to build an SVM model.”
From explanations to feature selection: assessing SHAP values as feature selection mechanism (2020, methodology): “Thus, we tested SHAP against three common feature selection approaches: Mutual Information [35]–[37], Recursive Feature Elimination [31], and ANOVA [38].”
Variable selection using random forests (2010, methodology): “Then some examples of embedded methods: Poggi et al. (2006) propose a method based on CART scores and using stepwise ascending procedure with elimination step; Guyon et al. (2002) and Rakotomanonjy (2003), propose meth-ods based on SVM scores and using descending elimination.”
An introduction to variable and feature selection (2003, methodology): “, 1992, Vapnik, 1998) with a regular training procedure and select features with backward elimination like in RFE (Guyon et al., 2002).”
The Bayesian elastic net (2010, background): “But in practice there are often studies that involve much more predictors than the sample size, e.g. microarray data analysis (Guyon et al. 2002).”
Grouped variable importance with random forests and application to multiple functional data analysis (2014, methodology): “The selection procedure is based on the Recursive Feature Elimination (RFE) algorithm proposed by Guyon et al. (2002) in the context of support vector machines.”