Cover Image

Cost-sensitive Feature Selection for Support Vector Machines

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Benítez-Peña, Sandra, Blanquero, Rafael, Carrizosa, Emilio, Ramírez-Cobo, Pepa
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

A cost-sensitive constrained Lasso
by: Blanquero, Rafael, et al.
Published: (2024)

Variable selection for Naïve Bayes classification
by: Blanquero, Rafael, et al.
Published: (2024)

Analysis of an aggregate loss model in a Markov renewal regime
by: Ramírez-Cobo, Pepa, et al.
Published: (2024)

The Relevance Feature and Vector Machine for health applications
by: Belenguer-Llorens, Albert, et al.
Published: (2024)

A Unified Approach to Extract Interpretable Rules from Tree Ensembles via Integer Programming
by: Bonasera, Lorenzo, et al.
Published: (2024)

Enhanced Feature Based Granular Ball Twin Support Vector Machine
by: Quadir, A., et al.
Published: (2024)

Soft decision trees for survival analysis
by: Consolo, Antonio, et al.
Published: (2025)

Optimizing Sparse Generalized Singular Vectors for Feature Selection in Proximal Support Vector Machines with Application to Breast and Ovarian Cancer Detection
by: Ugwu, Ugochukwu O., et al.
Published: (2024)

Granular Ball Twin Support Vector Machine
by: Quadir, A., et al.
Published: (2024)

Local Duality for Sparse Support Vector Machines
by: Zhang, Penghe, et al.
Published: (2026)

Investigating Quantum Feature Maps in Quantum Support Vector Machines for Lung Cancer Classification
by: Hafidi, My Youssef El, et al.
Published: (2025)

Multi-Task Learning Based on Support Vector Machines and Twin Support Vector Machines: A Comprehensive Survey
by: Bazikar, Fatemeh, et al.
Published: (2025)

Wasserstein Distributionally Robust Multiclass Support Vector Machine
by: Ibrahim, Michael, et al.
Published: (2024)

Trade-offs Between Individual and Group Fairness in Machine Learning: A Comprehensive Review
by: Benítez-Peña, Sandra, et al.
Published: (2026)

GBSVM: Granular-ball Support Vector Machine
by: Xia, Shuyin, et al.
Published: (2022)

Scaling Up ROC-Optimizing Support Vector Machines
by: Bae, Gimun, et al.
Published: (2025)

L1-Regularized Functional Support Vector Machine
by: Liu, Bingfan, et al.
Published: (2025)

Intuitionistic Fuzzy Generalized Eigenvalue Proximal Support Vector Machine
by: Quadir, A., et al.
Published: (2024)

Granular Ball Twin Support Vector Machine with Universum Data
by: Ganaie, M. A., et al.
Published: (2024)

Robust Universum Twin Support Vector Machine for Imbalanced Data
by: Tanveer, M., et al.
Published: (2024)

Multi-class Support Vector Machine with Maximizing Minimum Margin
by: Hao, Zhezheng, et al.
Published: (2023)

Support Vector Machine Classifier with Rescaled Huberized Pinball Loss
by: Diao, Shibo
Published: (2025)

L0-Regularized Quadratic Surface Support Vector Machines
by: Mousavi, Ahmad, et al.
Published: (2025)

Gap Safe Screening Rules for Fast Training of Robust Support Vector Machines under Feature Noise
by: Nguyen, Tan-Hau, et al.
Published: (2026)

Adiabatic Quantum Support Vector Machines
by: Date, Prasanna, et al.
Published: (2024)

Transformers as Support Vector Machines
by: Tarzanagh, Davoud Ataee, et al.
Published: (2023)

Elite-Driven Support Vector Machines for Classification
by: Jozani, Mohammad Jafari, et al.
Published: (2026)

Multi-Class Support Vector Machine with Differential Privacy
by: Park, Jinseong, et al.
Published: (2025)

Granular-Balls based Fuzzy Twin Support Vector Machine for Classification
by: Zhao, Lixi, et al.
Published: (2024)

Convex Loss Functions for Support Vector Machines (SVMs) and Neural Networks
by: Portera, Filippo
Published: (2026)

Fair Mixed Effects Support Vector Machine
by: Burgard, Jan Pablo, et al.
Published: (2024)

Robust Support Vector Machines for Imbalanced and Noisy Data via Benders Decomposition
by: Mohasel, Seyed Mojtaba, et al.
Published: (2025)

A fast learning algorithm for One-Class Slab Support Vector Machines
by: Kumar, Bagesh, et al.
Published: (2020)

The Stochastic Conjugate Subgradient Algorithm For Kernel Support Vector Machines
by: Zhang, Di, et al.
Published: (2024)

A Smoothing Algorithm for l1 Support Vector Machines
by: Emirahmetoglu, Ibrahim, et al.
Published: (2023)

Faster Algorithms for Structured Linear and Kernel Support Vector Machines
by: Gu, Yuzhou, et al.
Published: (2023)

Sparse Learning and Class Probability Estimation with Weighted Support Vector Machines
by: Zeng, Liyun, et al.
Published: (2023)

Optimization meets Machine Learning: An Exact Algorithm for Semi-Supervised Support Vector Machines
by: Piccialli, Veronica, et al.
Published: (2023)

Methods for Class-Imbalanced Learning with Support Vector Machines: A Review and an Empirical Evaluation
by: Rezvani, Salim, et al.
Published: (2024)

Support Vector Machine-Based Burnout Risk Prediction with an Interactive Interface for Organizational Use
by: Teodosio, Bruno W. G., et al.
Published: (2025)