Rough Cognitive Networks

Decision-making could informally be defined as the process of selecting the most appropriate actions among a set of possible alternatives in a given activity. In recent years several decision models based on Rough Set Theory (e.g. three-way decision rules) and Fuzzy Cognitive Maps have been introduced for addressing such problems. However, most of them are focused on decision-making problems with discrete attributes or they are oriented to specific domains. In this paper we present a decision model called Rough Cognitive Networks that combines the abstract semantic of the three-way decision model with the neural reasoning mechanism of Fuzzy Cognitive Maps for addressing numerical decision-making problems. The contribution of this study is two-fold. On one hand, it allows to explicitly handle decision-making problems with numerical features, where the target object could activate multiple regions at the same time. On the other hand, in such granular networks the three-way decision rules are used to design the topology of the map, addressing in some sense the inherent limitations in the expression and architecture of Fuzzy Cognitive Maps. Moreover, we propose a learning methodology using Harmony Search for adjusting the model parameters, leading to a parameter-free decision model where the human intervention is not required. A comparative analysis with standard classifiers and recently proposed rough recognition models is conducted in order to show the effectiveness of the proposal.