Gene Selection for Cancer Classification using Support Vector Machines

By Aixia Guo. 2014

3,753 citations recorded by the source corpus.

Subfields: Computational Biology & Bioinformatics, Medical & Health AI

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Citations in the corpus

Adversarial Feature Selection Against Evasion Attacks (2016, background): “may also significantly reduce computational complexity and allow for a better data understanding [37], [38].”
Interpreting support vector machine models for multivariate group wise analysis in neuroimaging (2015, positive): “While the weight map itself has been used 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…”
Benchmark and Survey of Automated Machine Learning Frameworks (2019, methodology): “Sometimes, high requirements for specific data qualities are introduced by later stages in an ML pipeline, e.g., SVMs require a numerical encoding of categorical features while random forests can handle them natively.”
Strongly agree or strongly disagree?: Rating features in Support Vector Machines (2016, background): “…Romero Morales (2013) for a recent review on Mathematical Optimization and the SVM, and Bertsimas et al. (2008), Brooks (2011), Cecchini et al. (2010), Chaovalitwongse et al. (2008), Guyon et al. (2002), Martens et al. (2007), Romero Morales and Wang (2010) for successful applications of the SVM.”
On Predicting Deletions of Microblog Posts (2015, methodology): “We therefore implement a recursive feature elimination algorithm [3] using DS1.”
Feature Selection via Probabilistic Outputs (2012, methodology): “This can be prohibitively expensive, so a variety of methods have been developed to approximate the result of this process (Guyon et al., 2002; Maldonado & Weber, 2009).”
An SVM Ensemble Approach Combining Spectral, Structural, and Semantic Features for the Classification of High-Resolution Remotely Sensed Imagery (2013, methodology): “Considering that the performance of the SVM classifier may be sensitive to the dimension of feature space [31], [35], a SVM-specific feature selection method, SVM recursive feature elimination (SVM-RFE) [20] is used for optimization for VS-SVM classification.”
Comparison of Deep Transfer Learning Strategies for Digital Pathology (2018, methodology): “To answer this question, we use cross-validated recursive feature elimination (RFE) [14] using importance scores derived from extremely randomized trees to rank the features (where the importance of a given feature is computed by adding up the weighted impurity decreases for all tree nodes where the”
Challenges in discriminating profanity from hate speech (2017, methodology): “In this manner, SVMs have been successfully applied in data mining and knowledge discovery in a wide range of tasks such as identifying discriminant cancer genes (Guyon et al. 2002).”
Does feature selection improve classification accuracy? Impact of sample size and feature selection on classification using anatomical magnetic resonance images (2012, methodology): “Recursive feature elimination is an iterative feature selection algorithm developed specifically for SVM (Guyon et al., 2002).”
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].”
A Review of Feature Selection and Feature Extraction Methods Applied on Microarray Data (2015, methodology): “C Bound is the decision boundary and it is used as a supplementary criterion in the case where different features in the subset have the same leave-one-out cross-validation error (LOOCVE) [26, 30, 31].”
A survey of feature selection and feature extraction techniques in machine learning (2014, methodology): “SVM-REF finds the m features which lead to the largest margin of class separation, and uses the weight vector w as a ranking criterion [19].”
Transfer Learning for Molecular Cancer Classification Using Deep Neural Networks (2019, methodology): “have suggested the use of a feature selection/ranking algorithm, followed by a classifier having high generalization ability [33], [34], [35], [36], [37], [38], [39], [40].”