Cauchy mutation based on objective variable of Gaussian particle swarm optimization for parameters selection of SVM

Wu, Qi; Law, Rob

doi:10.1016/j.eswa.2010.08.069

Cited by 27 publications

(9 citation statements)

References 21 publications

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“…This method provides better results compared to using only PSO. In 2011, this combined method was retested for SVM parameter selection [25][26][27]. The combined approach was also used to improve performance weaknesses in a process to identify a watermark image based on discrete cosine transform (DCT).…”

Section: Figure 1 Numeric Association Analysis Rule Miningmentioning

confidence: 99%

Improved optimization of numerical association rule mining using hybrid particle swarm optimization and cauchy distribution

Tahyudin¹,

Nambo²

2019

IJECE

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<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p>Particle Swarm Optimization (PSO) has been applied to solve optimization problems in various fields, such as Association Rule Mining (ARM) of numerical problems. However, PSO often becomes trapped in local optima. Consequently, the results do not represent the overall optimum solutions. To address this limitation, this study aims to combine PSO with the Cauchy distribution (PARCD), which is expected to increase the global optimal value of the expanded search space. Furthermore, this study uses multiple objective functions, i.e., support, confidence, comprehensibility, interestingness and amplitude. In addition, the proposed method was evaluated using benchmark datasets, such as the Quake, Basket ball, Body fat, Pollution, and Bolt datasets. Evaluation results were compared to the results obtained by previous studies. The results indicate that the overall values of the objective functions obtained using the proposed PARCD approach are satisfactory.</p></div></div></div>

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Section: Figure 1 Numeric Association Analysis Rule Miningmentioning

confidence: 99%

Improved optimization of numerical association rule mining using hybrid particle swarm optimization and cauchy distribution

Tahyudin¹,

Nambo²

2019

IJECE

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“…Cauchy was selected for experimentation as it is heavier tailed than Gaussian, which means that it has a tendency to produce values that fall farthest from its mean, which makes it more likely to escape premature convergence and increase search diversity. A system proposed by Wu and Law [16] used both Cauchy and Gaussian distributions when the PSO was deciding the next move of the particle. Q.…”

Section: Cauchy Distributionmentioning

confidence: 99%

Intelligent Facial Expression Recognition Using Particle Swarm Optimization Based Feature Selection

Robson

Zhang

2018

2018 IEEE Symposium Series on Computational Intelligence (SSCI)

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Particle Swarm Optimization (PSO) has become a popular method of feature selection in classification problems, due to its powerful search capability and computational simplicity. Classification problems, such as facial emotion recognition, often involve data sets containing high volumes of features, not all of which are useful for classification. Redundant and irrelevant features have the potential to negatively impact the performance and accuracy of facial emotion recognition systems. The feature selection process identifies the most relevant features to achieve improved classification performance. While the use of PSO as a feature selection method in facial emotion recognition systems has seen some successes, it is still susceptible to the issue of premature convergence. This work presents seven PSO variants which mitigate against the premature convergence problem through the incorporation of three random probability distributions (Cauchy, Gaussian and Lévy). At each iteration of the proposed PSO models, probability distributions are used to increase search diversity and reduce the number of redundant features used for classification. The seven PSO variants presented in this study have demonstrated positive results when tested on real world data sets, outperforming the standard PSO model and other related work within the field.

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“…The SVR would be would be used as an objective function for the PSO optimization process, to generate the outputs from the inputs. Some studies integrating PSO and SVM have been presented in the literatures [16][17][18][19][20][21][22]. However, there is still no research showing whether the hybrid model based on PSO and SVR can be applied in algal blooms problem that occurs in drinking water resources, which affects health of public.…”

Section: Introdutionmentioning

confidence: 98%

Integrating Support Vector Regression with Particle Swarm Optimization for numerical modeling for algal blooms of freshwater

Lou

Xie

Ung³

et al. 2015

Applied Mathematical Modelling

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Cauchy mutation based on objective variable of Gaussian particle swarm optimization for parameters selection of SVM

Cited by 27 publications

References 21 publications

Improved optimization of numerical association rule mining using hybrid particle swarm optimization and cauchy distribution

Improved optimization of numerical association rule mining using hybrid particle swarm optimization and cauchy distribution

Intelligent Facial Expression Recognition Using Particle Swarm Optimization Based Feature Selection

Integrating Support Vector Regression with Particle Swarm Optimization for numerical modeling for algal blooms of freshwater

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