Journal Article

·2021

Feature selection using stochastic approximation with Barzilai and Borwein non-monotone gains

Vural Aksakallı , Zeren D. Yenice YTU , Milad Malekipirbazari , Kamyar Kargar

Computers & Operations Research

No open-access abstract is available for this article. Use the DOI link on the right to read the full text.

Keywords

Simultaneous perturbation stochastic approximation Computer science Mathematical optimization Curse of dimensionality Feature selection Feature (linguistics) Convergence (economics) Algorithm Stochastic gradient descent Particle swarm optimization Overfitting Mathematics Artificial intelligence Stochastic process Statistics

Subject Areas

Machine Learning and Data Classification ·Artificial Intelligence ·Physical Sciences
Metaheuristic Optimization Algorithms Research ·Artificial Intelligence ·Physical Sciences
Neural Networks and Applications ·Artificial Intelligence ·Physical Sciences

Citations by Year

Related Articles

Other articles contributing to the same SDGs.

2026

Learning Nonlinear Transient Responses from Physics-Based Simulations: Surrogate Modeling with Tree-Based Ensembles for Finite-Horizon Emulation

SSRN Electronic Journal

2026

Toward improved siting of wind–solar hybrid farms: A novel framework integrating multi-criteria decision making, machine learning–driven feature selection, and spatial clustering

Applied Energy

2026

Improving missing transverse momentum estimation with a deep neural network

Physical review. D/Physical review. D.

2026

Residual-guided Fractional-Langevin Particle Swarm Optimization: A hybrid dynamics framework for global optimization

Swarm and Evolutionary Computation
All articles