Support vector machine parameter tuning based on particle swarm optimization metaheuristic

Korovkinas, Konstantinas; Danėnas, Paulius; Garšva, Gintautas

doi:10.15388/namc.2020.25.16517

Cited by 8 publications

(4 citation statements)

References 40 publications

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“…GA 35 and PSO 36 to train the hyperparameters of SVM and feature selection are available over a decade. Recently, Korovkinas et al 37 applied PSO to tune the cost(penalty) parameter of linear SVM, thereby improving the accuracy of classifying textual data. Additionally, the majority voting ensemble technique is used to boost the model's effectiveness.…”

Section: Methodsmentioning

confidence: 99%

Firefly-SVM predictive model for breast cancer subgroup classification with clinicopathological parameters

Sarkar,

Mali

2023

DIGITAL HEALTH

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Background Breast cancer is a highly predominant destructive disease among women characterised with varied tumour biology, molecular subgroups and diverse clinicopathological specifications. The potentiality of machine learning to transform complex medical data into meaningful knowledge has led to its application in breast cancer detection and prognostic evaluation. Objective The emergence of data-driven diagnostic model for assisting clinicians in diagnostic decision making has gained an increasing curiosity in breast cancer identification and analysis. This motivated us to develop a breast cancer data-driven model for subtype classification more accurately. Method In this article, we proposed a firefly-support vector machine (SVM) breast cancer predictive model that uses clinicopathological and demographic data gathered from various tertiary care cancer hospitals or oncological centres to distinguish between patients with triple-negative breast cancer (TNBC) and non-triple-negative breast cancer (non-TNBC). Results The results of the firefly-support vector machine (firefly-SVM) predictive model were distinguished from the traditional grid search-support vector machine (Grid-SVM) model, particle swarm optimisation-support vector machine (PSO-SVM) and genetic algorithm-support vector machine (GA-SVM) hybrid models through hyperparameter tuning. The findings show that the recommended firefly-SVM classification model outperformed other existing models in terms of prediction accuracy (93.4%, 86.6%, 69.6%) for automated SVM parameter selection. The effectiveness of the prediction model was also evaluated using well-known metrics, such as the F1-score, mean square error, area under the ROC curve, logarithmic loss and precision–recall curve. Conclusion Firefly-SVM predictive model may be treated as an alternate tool for breast cancer subgroup classification that would benefit the clinicians for managing the patient with proper treatment and diagnostic outcome.

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Section: Methodsmentioning

confidence: 99%

Firefly-SVM predictive model for breast cancer subgroup classification with clinicopathological parameters

Sarkar,

Mali

2023

DIGITAL HEALTH

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“…In the literature, a few studies improved the classification results of SVM using a variety of metaheuristics. Korovkinas et al (2020) utilized PSO to optimize parameter C of linear SVM, and their method obtained higher accuracy scores in classification results than a single method. In other studies, PSO was applied to select both parameters and features in SVM (Huang & Dun, 2008; Lin et al, 2008).…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting customer churn using grey wolf optimization‐based support vector machine with principal component analysis

Kurtcan

Özcan

2023

Journal of Forecasting

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Customer churn is a challenging problem that can lead to a loss of organizational assets. Organizations need to predict customer churn successfully in order to get rid of potential damages and gain a competitive advantage. The aim of this study is to provide a churn prediction model by including feature selection and optimization in classification. The study performs principal component analysis (PCA) to select the best features, support vector machine (SVM) to predict customer churn, and grey wolf optimization (GWO) to optimize the parameters of SVM. In other words, this study proposes a novel hybrid model called PCA‐GWO‐SVM to enhance the prediction ability in customer churn. A comparison experiment is carried out, evaluating the proposed model with the other classification algorithms. Experimental results show that the proposed PCA‐GWO‐SVM hybrid model produces higher accuracy, recall, and F1‐score than other machine learning algorithms such as logit, k‐nearest neighbors, naive Bayes, decision tree, and SVM.

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“…Minimizing variance will reduce the downward offset of asset returns; yet for the upward offset of asset returns, it will also eliminate part of it. Therefore, the variance and standard deviation method is likely to reduce the return on financial assets, and it mainly deals with the risks brought by high probability events in the financial market, and can't effectively measure the risks for some low probability events (Korovkinas et al, 2020;Xiang et al, 2021;Yi, 2021). However, low probability events in the financial market tend to have a huge impact on the return of financial assets and have a severe impact on financial institutions and investors (Faia et al, 2018).…”

Section: Risk Management Theory Of Financial Marketmentioning

confidence: 99%

CVaR Prediction Model of the Investment Portfolio Based on the Convolutional Neural Network Facilitates the Risk Management of the Financial Market

Wang¹,

Qiao

Huang

et al. 2021

Journal of Global Information Management

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In summary, firstly, a method for establishing a portfolio model is proposed based on the risk management theory of the financial market. Then, a prediction model for CVaR is established based on the convolutional neural network, and the improved particle swarm algorithm is employed to solve the model. The actual data analysis is implemented to prove the feasibility of CVaR prediction model based on deep learning and particle swarm optimization algorithm in financial market risk management. The test results show that the investment portfolio CVaR prediction model based on the convolutional neural network can obtain the optimal solution in the 18th generation at the fastest after using the improved particle swarm algorithm, which is more effective than the traditional algorithm. The CVaR prediction model of the investment portfolio based on the convolutional neural network facilitates the risk management of the financial market.

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Support vector machine parameter tuning based on particle swarm optimization metaheuristic

Cited by 8 publications

References 40 publications

Firefly-SVM predictive model for breast cancer subgroup classification with clinicopathological parameters

Firefly-SVM predictive model for breast cancer subgroup classification with clinicopathological parameters

Predicting customer churn using grey wolf optimization‐based support vector machine with principal component analysis

CVaR Prediction Model of the Investment Portfolio Based on the Convolutional Neural Network Facilitates the Risk Management of the Financial Market

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