Interval Type-3 Fuzzy Aggregation for Hybrid-Hierarchical Neural Classification and Prediction Models in Decision-Making

In all organizations, many decision analysts acquire their skills through the experience of facing challenges to structure complex problems. Therefore, every day, the use of tools to integrate indicators through multi-attribute ordering, component-based separation, and clustering to reduce the criteria required for decision-making and the achievement of goals and objectives is more frequent. Thus, our proposal consists of a new hybrid-hierarchical model for the classification and prediction of country indicators such as inflation, unemployment, population growth, and labor force, among others, in a decision-making environment using unsupervised neural networks and type-3 fuzzy systems. The contribution is achieving a type-3 fuzzy aggregation method in which the hierarchy is first represented by neural networks and later a set of type-1, type-2, and type-3 systems to combine the results, which allows multiple indicators to be separated and then integrated in an appropriate fashion. We can point out as one of the advantages of utilizing the method that the user can evaluate a range of qualities in multiple variables through the classification and prediction of time series attributes and assess a range of qualities for decision-making with uncertainty, according to the results of the simulations carried out.

Automated summary

This study proposes a new hybrid-hierarchical model using unsupervised neural networks and type-3 fuzzy systems for the classification and prediction of country indicators. The method achieves separation and integration of multiple indicators by representing the hierarchy with neural networks and combining the results with a set of fuzzy systems.