Generation of reducts and rules in multi-attribute and multi-criteria classification

The paper addresses the problem of analysing information tables which conta in objects described by both attributes and criteria, i.e. attributes with preference-ordered scales. The objects contained in those tables, represent ing exemplary decisions made by a decision maker or a domain expert, are us ually classified into one of several classes that are also often preference -ordered. Analysis of such data using the classic rough set methodology may produce improper results, as the original rough set approach is not able t o discover inconsistencies originating from consideration of typical criter ia, like e.g. product quality, market share or debt ratio. The paper presen ts the framework for the analysis of both attributes and criteria and a ver y promising algorithm for generating reducts. The algorithm presented is ev aluated in an experiment with real-life data sets and its results are compa red to those by two other reduct generating algorithms.