Abstract:

he classic theory of Rough sets is based on incomplete information systems. In practicing, decision tables are, however, usually incomplete due to the causes of data outputting or processing. That is to say, there are often default values. In order to deal with incomplete systems, Kryszkiewicz puts a Rough sets model on the basis of error tolerance relations. According to this model, constructing discernibility matrixes and discernibility functions are the familiar approach by the current knowledge reduction algorithms. By this means, all reductions can work out. But it has been proved that it is a problem of “NPhard”. So it is more effective when a heuristic search algorithm is used to attain the most optimized or the second most optimized reduction. In this paper, the importance of attributes is defined and used as heuristic information. Then a complete knowledge reduction algorithm is put forward.

Key words: Rough sets, incomplete decision table, knowledge reduction