Attribute Reduction Algorithm Using Rough Sets

In order to overcome the incompleteness of reduction definition in classical rough sets theory and no avail of getting the optimal attribute reduction,a new measure of attribute significance is put forward and a concept of decision power is introduced.Accordingly,an improved heuristic algorithm for attribute reduction based on information entropy is proposed.It is analyzed in theory and tested in practice.By means of the test data analysis on CTCS-2 train control center software test platform,the optimal attribute reduction can be successfully obtained by the algorithm,latent relations and rules are also discovered and decision rules are given.It contributes a lot to make more effective decision analysis.