Asymmetric Possibility and Necessity Regression by Twin-Support Vector Networks

This article proposes a novel asymmetric dual-regression model that combines the principles of twin-support vector machine theory with the possibilistic regression analysis. Using the principle of a twin-support vector machine, the proposed approach solves four smaller quadratic programming problems, each of which constructs the lower and upper bound functions of the possibility and necessity models, rather than a single large one. This strategy significantly reduces the time that is required for training. The output from the obtained dual-regression model is characterized by an asymmetric trapezoidal fuzzy number. The obtained asymmetric dual-regression model is more flexible and models the data distribution better than a symmetric model. The proposed approach provides a unified framework that accepts various types of crisp and fuzzy input variables by using radial kernels. The proposed dual model also indicates a degree of confidence to the predicted outputs. The explicable characteristic for the degree of confidence also means that the proposed approach is more suitable for decision-making task. The experimental results demonstrate that the proposed approach has a more efficient training procedure and better describes the inherent ambiguity in the observed phenomena.

Automated summary

This article introduces a novel asymmetric dual-regression model that combines twin-support vector machine theory with the principles of possibilistic regression analysis, providing better modeling of data distribution and confidence measure for predicted outputs. The proposed approach efficiently solves multiple smaller problems during training, leading to reduced time cost.