Particle swarm optimization for parameter determination and feature selection of support vector machines

By Shih-Wei Lin, Kuo-Ching Ying, Shih-Chieh Chen, Zne-Jung Lee. Expert systems with applications, 2008

886 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/48732e566f1027b5dcc1e037fb0058a31acab438

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In Artificial Intelligence

In Computer Vision and Pattern Recognition

In Control and Systems Engineering

Citations in the corpus

A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring (2017, methodology): “A few generic optimization approaches, such as genetic algorithm and particle swarm optimization, have been applied for the hyper-parameter optimization of SVM-based credit scoring models (Huang et al., 2007; Lin et al., 2008).”
Particle Swarm Optimization for Feature Selection in Classification: A Multi-Objective Approach (2013, positive): “Later, Lin et al. [38] propose a wrapper feature selection algorithm (PSO+SVM) using PSO and SVM.”
Hyperparameter Search in Machine Learning (2015, methodology): “A wide variety of optimization methods have been used for hyperparameter search, including particle swarm optimization [26, 27], genetic algorithms [32], coupled simulated annealing [33] and racing algorithms [6].”
Metaheuristic design of feedforward neural networks: A review of two decades of research (2017, methodology): “In [226], a modified version of binary PSO was proposed where EA like mutation operator was applied to mutated binary vectors.”
SVM Parameter Optimization using Grid Search and Genetic Algorithm to Improve Classification Performance (2016, methodology): “According to [11], the cross-validation technique can prevent the over-fitting problem.”
Theory and Application of Magnetic Flux Leakage Pipeline Detection (2015, background): “The network has the function of being the approximation of the functional approximation and the optimal functional approximation property [108].”
Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders (2013, methodology): “[13] and can be defined as follows:”
Feature selection via a novel chaotic crow search algorithm (2017, methodology): “However, for the other algorithms such as PSO, they have many parameters.”
Scene classification based on single-layer SAE and SVM (2015, methodology): “Paper (Lin et al., 2008) introduce the PSO algorithm which can achieve precise optimal solution in multi-dimension optimization problem to the selecting of SVM parameters (C and c) in 2008.”
Taking Human out of Learning Applications: A Survey on Automated Machine Learning (2018, methodology): “n AutoML. For example, evolutionary algorithms has been applied in feature selection and generation [50], [86], [87], [88], [89], and model selection [90]. PSO has been used for model selection [53], [91], feature selection for support vector machine (SVM) [91], and hyper-parameter tuning for deep n”
Financial credit risk assessment: a recent review (2015, methodology): “Swarm intelligence represented by ant colony optimization and particle swarm optimization was employed to automatically select the optimal features used by the subsequent machine learning models (Lin et al. 2008; Marinakis et al. 2009.”