Double regularization methods for robust feature selection and SVM classification via DC programming

DOI10.1016/j.ins.2017.11.035zbMath1436.68310OpenAlexW2769730088MaRDI QIDQ781867

Sebastián Maldonado, Julio López, Miguel Carrasco

Publication date: 20 July 2020

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2017.11.035

zbMATH Keywords

support vector machinessecond-order cone programmingDC algorithmzero norm

Mathematics Subject Classification ID

Semidefinite programming (90C22) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (9)

Robust projection twin support vector machine via DC programming ⋮ Optimal arrangements of hyperplanes for SVM-based multiclass classification ⋮ Global optimization on non-convex two-way interaction truncated linear multivariate adaptive regression splines using mixed integer quadratic programming ⋮ Dependency maximization forward feature selection algorithms based on normalized cross-covariance operator and its approximated form for high-dimensional data ⋮ Inconsistency guided robust attribute reduction ⋮ A safe reinforced feature screening strategy for Lasso based on feasible solutions ⋮ Variable selection in classification for multivariate functional data ⋮ Profit-based churn prediction based on minimax probability machines ⋮ D.C. programming for sparse proximal support vector machines

Uses Software

• SeDuMi

Cites Work

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