Twin support vector machine improves the performance of the classifier by solving the smaller quadratic programming problem. However

this method mainly utilizes the separability between classes and constructs the corresponding model using the hinge loss function. Not considering the structural information of the intra-class data and the influence of different samples on the classification

the method has strong sensitivity to noise and instability of resampling. In order to further improve the performance of the twin support vector machine

the structural information of different classes in the data and the effects of different samples are introduced into the twin support vector machine based on the pinball loss function

and the structure fuzzy support vector machine model based on pinball loss is obtained. The structural fuzzy twin support vector machine algorithm pin-sftsvm based on the pinball loss is derived theoretically. The presented algorithm pin-sftsvm is tested by selecting the artificially generated data set and the UCI standard data set

and compared with the tbsvm

s-tsvm and pin-tsvm algorithms. Experimental results show the effectiveness of the proposed algorithm.