A wrapper method for feature selection using Support Vector Machines

By Sebastián Maldonado, Richard Weber. 2009

465 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Molecular Biology

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Citations in the corpus

EEG Based Emotion Recognition: A Tutorial and Review (2022, background): “The top vital features can be reserved for further model design, while the others will be abandoned [112, 113].”
Robust Joint Graph Sparse Coding for Unsupervised Spectral Feature Selection (2017, background): “For example, Maldonado and Weber [35] proposed to find a subset of all the features by maximizing the performance of an Support Vector Machine (SVM) classifier.”
mr2PSO: A maximum relevance minimum redundancy feature selection method based on swarm intelligence for support vector machine classification (2011, background): “In the classification phase, a point x is assigned a label y according to y = sgn[w x + b].”
Feature selection for multi-label naive Bayes classification (2009, methodology): “Note that other feature selection techniques may also be used in place of PCA and GA [25,6,36,19] in order to enhance MLNB-BASIC.”
An embedded feature selection method for imbalanced data classification (2019, methodology): “Instead of using a single wrapper method such as forward selection, the study [19] presents a novel method, called a simulated annealing generic algorithm (SAGA), which incorporates existing wrapper methods into a single solution.”
Feature selection for high-dimensional class-imbalanced data sets using Support Vector Machines (2014, methodology): “The corresponding dual formulation can be stated as follows: We base our analysis on the Gaussian kernel, which has led to the best results in many applications and is a common choice in the literature [20].”
Advanced conjoint analysis using feature selection via support vector machines (2015, unknown): “[36, 44] 3 [6, 16, 39] 3 [3, 5, 13, 17, 19, 20, 26, 29, 31, 35, 43] 3 [7, 22, 32, 33] 3 3 [10, 11, 14, 15, 42] 3 3 [2] 3 3 This study 3 3 3”
Selection of time instants and intervals with Support Vector Regression for multivariate functional data (2020, background): “…(Dash et al., 2002; Dash and Liu, 2000; Dash et al., 1997; Law et al., 2004, 2003; Li et al., 2008) and supervised classiﬁcation (Aytug, 2015; Benítez-Peña et al., 2019; Bertolazzi et al., 2016; Carrizosa et al., 2011; Dash and Liu, 1997; Maldonado and Weber, 2009; Maldonado et al., 2011).”
Human–Agent Teaming for Multirobot Control: A Review of Human Factors Issues (2014, methodology): “Wrapper methods include Genetic Algorithms (see [28]), SVM (see [6], [29], [30]), the RFE method, and Boosting.”
Exploring EEG Features in Cross-Subject Emotion Recognition (2018, background): “…are re-ranked according to a pre-deﬁned ranking criteria, e.g., based on the degree of correlation between the feature and the target class or based on the value of the feature weight, and then, the features above a pre-deﬁned threshold are selected (Huang et al., 2006; Maldonado and Weber, 2008).”