Redução de valores no critério de decisão em aplicações de Rough Sets com dominância e seus impactos na qualidade da aproximação

Abstract

Creating rules for the support of the decision process is the main subject of Rough Sets Theory. The study first published by Pawlak in 1982 was a catalyst of several studies focusing on creating rules for the support of the decision making process based on multiple attributes conditioning one or more decision attributes. As the studies evolved, attributes with the feature of dominance – attributes measured in some scale – have caught attention. This gave rise to DRSA (Dominance-based Rough Sets Approach). The excessively strict guidelines of DRSA original studies led to the creation of new guidelines that consider not only deterministic rules – 100% certainty – but also probabilistic rules, which account for a certain degree of uncertainty. There are other developments of DRSA and the approach here taken evaluates alternatives for enhancing the quality of the approximation evaluation, therefore enhancing the quality of the rules, by clustering classes of values of decision attributes without compromising the guiding principles of DRSA. According to the need of researcher, the reduction of classes can be used in conjunction with other alternatives of development of DRSA. Two different proposals for the clustering of attributes are presented and evaluated in this study, one based on density functions and the other based on probabilistic transformations.