FSVM

Acronym	Definition
FSVM	Food Supply Veterinary Medicine (various organizations)
FSVM	Fellow of the Society for Vascular Medicine
FSVM	Fédération Suisse de Vol à Moteur (Swiss flying federation)
FSVM	Fuzzy Support Vector Machine (pattern analysis)
FSVM	Full Service Vending Machine (lotteries)
FSVM	Four Seasons Vineyard Management (Geyserville, CA)

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References in periodicals archive ?

The highest accuracy, sensitivity, specificity, F-measure, recall, and precision without resampling are achieved by FMLP, FSVM, FMLP, FMLP, FMLP, and FMLP, respectively, with the values 85%, 88.4%, 74.2%, 83.3%, 85%, and 83.9%.

The highest accuracy, sensitivity, specificity, F-measure, recall, and precision without resampling are achieved by FMLP, FSVM, FMLP, PSVM, FMLP, and FMLP, respectively, with the values 82.4%, 88%, 83.3%, 80.6%, 82.4%, and 82.6%.

On the contrary, with resampling, FMLP shows the best accuracy (98%), but for all the other parameters, like sensitivity (96.61%), F-measure (98%), recall (98%), and precision (98%), PMLP (FMLP) shows best results and PSVM (GSVM and FSVM) shows best results for specificity (99.55%).

PMLP shows best results for specificity (90.55%), F-measure (86.77%), recall (87.1%), and precision (86.6%), whereas GSVM (PSVM and FSVM) shows better results for the parameters like accuracy (87.1%) and sensitivity (73.08%) without resampling.

Hybrid Disease Diagnosis Using Multiobjective Optimization with Evolutionary Parameter Optimization

Zhang, "New facial expression recognition based on FSVM and KNN," Optik, vol.

Weighted feature Gaussian Kernel SVM for emotion recognition

The average classification accuracy of PFRSVM is better than SVM, FSVM and MFSVM for Gaussian RBF and Bayesian kernels.

Dataset SVM FSVM MFSVM PFRSVM German Credit 78.3 80.5 82.9 94.8 Heart Disease 83.5 86.9 87.0 95.9 Ionosphere 85.7 87.7 89.0 96.5 Semeion Handwritten Digit 82.7 86.0 86.5 95.5 Landsat Satellite 86.8 86.9 87.2 96.2 Table 8: The Average Classification Accuracy Percentage of different Versions of SVM with Bayesian Kernel.

Parallel fuzzy rough support vector machine for data classification in cloud environment

Dai, "FSVM based recognition approach for downhole pump dynamometer cards," Oil Field Equipment, vol.

Research on feature extraction of indicator card data for sucker-rod pump working condition diagnosis

As a result, the hyperplane of FSVM can be skewed towards the minority class, and this skewness can degrade the performance of FSVM with respect to the minority class.

In this paper, we propose a new FSVM method for the class imbalance problem (FSVM-CIP) which can be used to address both the problem of class imbalance and outliers/noise.

Therefore, FSVM can find a more robust hyperplane by maximizing the margin by letting some misclassification of less important points.

FSVM for the Class Imbalance Problem in the Linear Case

New fuzzy support vector machine for the class imbalance problem in medical datasets classification