Parameters optimization of SVM based on the swarm intelligence

Wang, Yuxin; Li, Xuezhen; Wang, Zhengyi

doi:10.1088/1742-6596/1437/1/012005

Cited by 8 publications

(2 citation statements)

References 9 publications

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“…A modified mutation strategy-based flower pollination algorithm (MFPA) has been successfully applied to optimize SVM parameters in [23]. The MFPA algorithm is used to determine and optimize the penalty coefficient and kernel of SVM parameters to obtain the best combination of SVM parameters.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Feature Selection Optimization Model for High Dimension Data Classification

et al. 2021

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Feature selection is an NP-hard combinatorial problem, in which the number of possible feature subsets increases exponentially with the number of features. In the case of large dimensionality, the goal of feature selection is to determine the smallest possible features considering the most informative subset. In this paper, we proposed a hybrid feature selection optimization model for Cancer Classification called, ENSVM. Our model is based on using the Elastic Net (EN) method that regulates and selects variables for gene selection of genomic microarray data. We applied three different optimization techniques namely Social Ski-Driver (SSD), Randomized SearchCV (RS) and Elastic NetCV (ENCV) for determining Elastic Net with traditional Support Vector Machines for classification. To evaluate the model, we compared the results of applying ENSVM to seven genomic microarray data with the SSD-SVM model and SVM with (RBF) kernel without any feature selection method. The results of the comparison revealed the effect of ENSVM in selecting the optimal feature subset that maximized the classification performance. Accordingly, minimizing the number of features is significant when analyzing high dimensional data for performance nevertheless accuracy. Moreover, the ENSVM model is superior compared with the SSD-SVM model.

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Section: Related Workmentioning

confidence: 99%

A Hybrid Feature Selection Optimization Model for High Dimension Data Classification

et al. 2021

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“…For instance, Tran et al [14] analyzed the impact of parameters optimization on the performance of traditional classification algorithms. In this regard, several optimization techniques have also been employed for optimizing the parameters of Support Vectors Machines (SVMs) [15]- [17]. Several methods have also been proposed for the optimization of different parameters of Artificial Neural Networks (ANNs) [18]- [20].…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimized Federated Learning For Industrial IoT and Smart City Services

Qolomany¹,

Ahmad²,

Al-Fuqaha³

et al. 2020

Preprint

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Most of the research on Federated Learning (FL) has focused on analyzing global optimization, privacy, and communication, with limited attention focusing on analyzing the critical matter of performing efficient local training and inference at the edge devices. One of the main challenges for successful and efficient training and inference on edge devices is the careful selection of parameters to build local Machine Learning (ML) models. To this aim, we propose a Particle Swarm Optimization (PSO)-based technique to optimize the hyperparameter settings for the local ML models in an FL environment. We evaluate the performance of our proposed technique using two case studies. First, we consider smart city services, and use an experimental transportation dataset for traffic prediction as a proxy for this setting. Second, we consider Industrial IoT (IIoT) services, and use the real-time telemetry dataset to predict the probability that a machine will fail shortly due to component failures. Our experiments indicate that PSO provides an efficient approach for tuning the hyperparameters of deep Long short-term memory (LSTM) models when compared to the grid search method. Our experiments illustrate that the number of clients-server communication rounds to explore the landscape of configurations to find the near-optimal parameters are greatly reduced (roughly by two orders of magnitude needing only 2%-4% of the rounds compared to state of the art non-PSO-based approaches). We also demonstrate that utilizing the proposed PSO-based technique to find the near-optimal configurations for FL and centralized learning models does not adversely affect the accuracy of the models.

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Towards Communication-Efficient Federated Learning Through Particle Swarm Optimization and Knowledge Distillation

Zaman,

Talukder,

Hossain

et al. 2024

2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC)

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Parameters optimization of SVM based on the swarm intelligence

Cited by 8 publications

References 9 publications

A Hybrid Feature Selection Optimization Model for High Dimension Data Classification

A Hybrid Feature Selection Optimization Model for High Dimension Data Classification

Particle Swarm Optimized Federated Learning For Industrial IoT and Smart City Services

Towards Communication-Efficient Federated Learning Through Particle Swarm Optimization and Knowledge Distillation

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