Improved twin support vector machine algorithm and applications in classification problems

The distribution of data has a significant impact on the results of classification. When the distribution of one class is insignificant compared to the distribution of another class, data imbalance occurs. This will result in rising outlier values and noise. Therefore, the speed and performance of classification could be greatly affected. Given the above problems, this paper starts with the motivation and mathematical representing of classification, puts forward a new classification method based on the relationship between different classification formulations. Combined with the vector characteristics of the actual problem and the choice of matrix characteristics, we firstly analyze the orderly regression to introduce slack variables to solve the constraint problem of the lone point. Then we introduce the fuzzy factors to solve the problem of the gap between the isolated points on the basis of the support vector machine. We introduce the cost control to solve the problem of sample skew. Finally, based on the bi-boundary support vector machine, a two-step weight setting twin classifier is constructed. This can help to identify multitasks with feature-selected patterns without the need for additional optimizers, which solves the problem of large-scale classification that can't deal effectively with the very low category distribution gap.