High dimensional data classification and feature selection using support vector machines

DOI10.1016/j.ejor.2017.08.040zbMath1381.62170OpenAlexW2756063524MaRDI QIDQ1681156

Bissan Ghaddar, Joe Naoum-Sawaya

Publication date: 23 November 2017

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ejor.2017.08.040

zbMATH Keywords

classificationsupport vector machinesmachine learningfeature selectionanalytics

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of mathematical programming (90C90) Learning and adaptive systems in artificial intelligence (68T05) Quadratic programming (90C20)

Related Items (17)

Preference estimation under bounded rationality: identification of attribute non-attendance in stated-choice data using a support vector machines approach ⋮ Performance analysis of the compressed distributed least squares algorithm ⋮ Optimal arrangements of hyperplanes for SVM-based multiclass classification ⋮ Quadratic surface support vector machine with L1 norm regularization ⋮ ABC classification using extended R-model, SVM and Lorenz curve ⋮ Random forest kernel for high-dimension low sample size classification ⋮ Cost-sensitive feature selection for support vector machines ⋮ Robust low-rank multiple kernel learning with compound regularization ⋮ Sparse regression for large data sets with outliers ⋮ Simultaneous feature selection and clustering based on square root optimization ⋮ Evaluating and selecting features via information theoretic lower bounds of feature inner correlations for high-dimensional data ⋮ Optimization problems for machine learning: a survey ⋮ A novel embedded min-max approach for feature selection in nonlinear support vector machine classification ⋮ Direct symbol decoding using GA-SVM in chaotic baseband wireless communication system ⋮ Assessment of the influence of features on a classification problem: an application to COVID-19 patients ⋮ Data-Driven Preference Learning Methods for Value-Driven Multiple Criteria Sorting with Interacting Criteria ⋮ Unsupervised quadratic surface support vector machine with application to credit risk assessment

Uses Software

• UCI-ml

Cites Work

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