Analysis of distributed databases with a hybrid rough sets approach

The aim of this paper is to offer mathematical proofs of Pawlakpsilas rough set theory about distributed knowledge based on rough sets and relational databases. A case study on actual self-reported geriatric data for survival analysis is presented to provide a computational evidence of the distributed knowledge. Risk factors, prolongation time prediction rules and validation are also computed and discussed. We illustrate that dividing a decision table (or database) into smaller units will in general result in the loss of some information by rough set theory

Rule learning: Ordinal prediction based on rough sets and soft-computing

AbstractThis work promotes a novel point of view in rough set applications: rough sets rule learning for ordinal prediction is based on rough graphical representation of the rules. Our approach tackles two barriers of rule learning. Unlike in typical rule learning, we construct ordinal prediction with a mathematical approach, rough sets, rather than purely rule quality measures. This construction results in few but significant rules. Moreover, the rules are given in terms of ordinal predictions rather than as unique values. This study also focuses on advancing rough sets theory in favor of soft-computing. Both theoretical and a designed architecture are presented. The features of our proposed approach are illustrated using an experiment in survival analysis. A case study has been performed on melanoma data. The results demonstrate that this innovative system provides an improvement of rule learning both in computing performance for finding the rules and the usefulness of the derived rules