Automatic detection of erythemato-squamous diseases using PSO–SVM based on association rules

Abdi, Mohammad Javad; Giveki, Davar

doi:10.1016/j.engappai.2012.01.017

Cited by 124 publications

(50 citation statements)

References 23 publications

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“…The LSSVM model is a regression prediction method with nonlinear characteristics based on a statistical learning theory, and it is regarded as an improved form of the SVM (Vapnik, 1995;Abdi and Giveki, 2013). First, after dividing the sample data into training samples and testing samples, the training samples are plotted in a high-dimensional feature space via nonlinear mapping.…”

Section: Lssvmmentioning

confidence: 99%

Landslide displacement prediction using the GA-LSSVM model and time series analysis: a case study of Three Gorges Reservoir, China

Wen

Tang

Wang

et al. 2017

Nat. Hazards Earth Syst. Sci.

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Abstract. Predicting landslide displacement is challenging, but accurate predictions can prevent casualties and economic losses. Many factors can affect the deformation of a landslide, including the geological conditions, rainfall and reservoir water level. Time series analysis was used to decompose the cumulative displacement of landslide into a trend component and a periodic component. Then the least-squares support vector machine (LSSVM) model and genetic algorithm (GA) were used to predict landslide displacement, and we selected a representative landslide with episodic movement deformation as a case study. The trend component displacement, which is associated with the geological conditions, was predicted using a polynomial function, and the periodic component displacement which is associated with external environmental factors, was predicted using the GA-LSSVM model. Furthermore, based on a comparison of the results of the GA-LSSVM model and those of other models, the GA-LSSVM model was superior to other models in predicting landslide displacement, with the smallest root mean square error (RMSE) of 62.4146 mm, mean absolute error (MAE) of 53.0048 mm and mean absolute percentage error (MAPE) of 1.492 % at monitoring station ZG85, while these three values are 87.7215 mm, 74.0601 mm and 1.703 % at ZG86 and 49.0485 mm, 48.5392 mm and 3.131 % at ZG87. The results of the case study suggest that the model can provide good consistency between measured displacement and predicted displacement, and periodic displacement exhibited good agreement with trends in the major influencing factors.

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Section: Lssvmmentioning

confidence: 99%

Landslide displacement prediction using the GA-LSSVM model and time series analysis: a case study of Three Gorges Reservoir, China

Wen

Tang

Wang

et al. 2017

Nat. Hazards Earth Syst. Sci.

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“…The model issuitablefor nonlinear high-dimensional data modeling problems and can be applied forpredicting the landslide deformation [19]. The pertinent points of the SVM are presented here [20].…”

Section: Rbf-svmmentioning

confidence: 99%

Landslide Displacement Prediction Affected by the Periodic Precipitation, Reservoir Level and Groundwater Level Fluctuations

Ren¹,

Wu²

2014

IJUNESST

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“…In the first stage optimal feature were extracted using association rule and in second phase the PSO was used to discovered best kernel parameters for SVM in order to improve the accuracy of classifier model [41].…”

Section: Literature Reviewmentioning

confidence: 99%

Data Mining for diagnosis in Healthcare Sector-a review

2017

IJASRE

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Automatic detection of erythemato-squamous diseases using PSO–SVM based on association rules

Cited by 124 publications

References 23 publications

Landslide displacement prediction using the GA-LSSVM model and time series analysis: a case study of Three Gorges Reservoir, China

Landslide displacement prediction using the GA-LSSVM model and time series analysis: a case study of Three Gorges Reservoir, China

Landslide Displacement Prediction Affected by the Periodic Precipitation, Reservoir Level and Groundwater Level Fluctuations

Data Mining for diagnosis in Healthcare Sector-a review

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