Combined SVM-Based Feature Selection and Classification

By Julia Neumann, Christoph Schnörr, Gabriele Steidl. 2005

279 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Adversarial Feature Selection Against Evasion Attacks (2016, neutral): “tures during classifier training [59], [60].”
Hybrid attribute reduction based on a novel fuzzy-rough model and information granulation (2007, methodology): “CART and SVM are introduced to evaluate the selected features.”
MIXED-INTEGER SUPPORT VECTOR MACHINE (2009, methodology): “Embedded algorithms augment the SVM formulation, and seek to learn the optimal feature subset as well as the SVM classifier simultaneously [3, 7, 17, 18, 22, 23], such as Feature Selection Concave (FSV), and L1-norm SVM, etc.”
A rough set approach to feature selection based on ant colony optimization (2010, unknown): “: +86 21 69589359. yon et al., 2002; Neumann et al., 2005; Gasca et al., 2006; Xie et al., 2006) and classifier independent (Kira and Rendell, 1992; Modrzejewski, 1993; Dash and Liu, 2003).”
A Proof of Convergence of the Concave-Convex Procedure Using Zangwill's Theory (2012, background): “However, the assumptions on u and v as mentioned in theorem 3 are usually satisfied in machine learning applications, the examples of which include sparse principal component analysis (Sriperumbudur et al., 2007), feature selection in SVMs (Neumann et al., 2005), and transductive SVMs (Collobert et ”
Supervised feature selection via dependence estimation (2007, background): “For computational convenience the normalisation is often omitted in practise (Neumann et al., 2005), which leaves us with tr K L .”
Neighborhood rough set based heterogeneous feature subset selection (2008, methodology): “The representative is attribute reduction based on rough set theory [24,44], while the latter views all attributes as real-valued variables, which take values in the real-number spaces [21,22].”
Feature Selection via Dependence Maximization (2012, background): “Indeed, for computational convenience the normalization is often omitted in practise (Neumann et al., 2005), which leaves us with trKL, the corresponding estimator of MMD.”
On the Convergence of the Concave-Convex Procedure (2009, methodology): “Other applications where CCCP has been used include sparse principal component analysis [27], transductive SVMs [11, 5, 28], feature selection in SVMs [22], structured estimation [10], missing data problems in Gaussian processes and SVMs [26], etc.”
Parameter determination of support vector machine and feature selection using simulated annealing approach (2008, background): “[33] observed that feature selection is a significant combinatorial optimization problem in supervised pattern classification, and proposed an additional regularization and embedded nonlinear feature selection method based on the difference of convex functions programming, which is a general framewo”
Discriminative Semi-Supervised Feature Selection Via Manifold Regularization (2009, methodology): “A number of performance criteria have been proposed for feature selection, including mutual information [19], [31], maximum margin [22], [52], kernel alignment [15], [37], graph-spectrum based measures [53], [66], construction errors in neural network [35], [42], worst case probability [4], [61], an”