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.
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