Twin support vector machine based on improved artificial fish swarm algorithm with application to flame recognition

Gao, Yikai; Xie, Lihua; Zhang, Zhengdao; Fan, Qigao

doi:10.1007/s10489-020-01676-6

Cited by 29 publications

(6 citation statements)

References 41 publications

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“…When the data is unbalanced, that is, the numbers of positive samples and negative samples are much different, SVM often fails to achieve the expected results. However, TWSVM still performs well in this case, mainly because the TWSVM has two penalty factors in the hyperplane, which can adjust the penalty degree of the hyperplane at the same time [25]. The recognition results of the TWSVM algorithm are closer to the true values of the sequence.…”

Section: Related Workmentioning

confidence: 97%

Study on Gene Splicing Site Recognition Based on Particle Swarm Optimization Twin Support Vector Machine Algorithm for Smart Healthcare

Zhang

Wang

Mei

et al. 2023

Wireless Communications and Mobile Computing

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Gene splicing site recognition is a very important research topic in smart healthcare. Gene splicing site recognition is of great significance, not only for the large-scale and high-quality computational annotation of genomes but also for the analysis and recognition of the gene sequences evolutionary process. It is urgent to study a reliable and effective algorithm for gene splice site recognition. Traditional Twin Support Vector Machine (TWSVM) algorithm has advantages in solving small-sample, nonlinear, and high-dimensional problems, but it cannot deal with parameter selection well. To avoid the blindness of parameter selection, particle swarm optimization algorithm was used to find the optimal parameters of twin support vector machine. Therefore, a Particle Swarm Optimization Twin Support Vector Machine (PSO-TWSVM) algorithm for gene splicing site recognition was proposed in this paper. The proposed algorithm was compared with traditional Support Vector Machine algorithm, TWSVM algorithm, and Least Squares Support Vector Machine algorithm. The comparison results show that the positive sample recognition rate, negative sample recognition rate, and correlation coefficient (CC) of the proposed algorithm are the best among the four different support vector machine algorithms. The proposed algorithm effectively improves the recognition rate and the accuracy of splice sites. The comparison experiments verify the feasibility of the proposed algorithm.

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Section: Related Workmentioning

confidence: 97%

Study on Gene Splicing Site Recognition Based on Particle Swarm Optimization Twin Support Vector Machine Algorithm for Smart Healthcare

Zhang

Wang

Mei

et al. 2023

Wireless Communications and Mobile Computing

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“…In this experiment, the proposed adaptive particle swarm optimization-TWSVM (APSO-TWSVM) method uses meteorological data to predict the deformation of the surface of the mine slope. TWSVM inputs are collected meteorological data such as the temperature, atmospheric pressure, cumulative rainfall, relative humidity, and refractive index [23].…”

Section: Apso-twsvm Prediction Modelmentioning

confidence: 99%

Surface Deformation Prediction Model of High and Steep Open-Pit Slope Based on APSO and TWSVM

Du,

Song,

et al. 2024

ELEKTRON ELEKTROTECH

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At present, due to the complex and changeable geological conditions, the precise deformation prediction technology of high and steep slope could not achieve an accurate prediction. In particular, the single forecasting model has some problems such as poor stability, low precision, and data fluctuation. In practice, excavating the complex nonlinear relationship between open-pit slope surface deformation monitoring data and various influencing factors and improving the accuracy of the deformation prediction of high and steep slopes is the key to safe open-pit mine production. It proposed to introduce the position factor and the velocity factor into a twin support vector machine (TWSVM). The adaptive subgroup optimisation (APSO) algorithm is selected for parameter optimisation. Through the comparative analysis of TWSVM, genetic algorithm-TWSVM (GA-TWSVM), and the proposed APSO⁃TWSVM, the experimental data show that the mean absolute error (MAE) values of the three models are 13.29 %，8.17 %, and 1.27 %, the RMSE - 47.83 %，6.52 %, and 3.02 %, respectively; the prediction time for APSO⁃TWSVM is improved by 62.5 % compared to GA-TWSVM.

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“…To verify the superior performance of the proposed DGA-ACBIO in terms of search speed, accuracy and number of iterations, simulations of 20 tea fields were executed and compared using the dynamic genetic algorithm, ant colony binary iterative algorithm, particle swarm algorithm (Zeng et al, 2020;Zhou et al, 2022) and artificial fish swarm algorithm (Gao et al, 2020).…”

Section: Dynamic Genetic Algorithm With Ant Colony Binary Iterative O...mentioning

confidence: 99%

A scheduling route planning algorithm based on the dynamic genetic algorithm with ant colony binary iterative optimization for unmanned aerial vehicle spraying in multiple tea fields

Liu

Zhang

et al. 2022

Front. Plant Sci.

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Twin support vector machine based on improved artificial fish swarm algorithm with application to flame recognition

Cited by 29 publications

References 41 publications

Study on Gene Splicing Site Recognition Based on Particle Swarm Optimization Twin Support Vector Machine Algorithm for Smart Healthcare

Study on Gene Splicing Site Recognition Based on Particle Swarm Optimization Twin Support Vector Machine Algorithm for Smart Healthcare

Surface Deformation Prediction Model of High and Steep Open-Pit Slope Based on APSO and TWSVM

A scheduling route planning algorithm based on the dynamic genetic algorithm with ant colony binary iterative optimization for unmanned aerial vehicle spraying in multiple tea fields

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