Entropy based attribute reduction approach for incomplete decision table

In this paper, a new entropy based uncertainty measure is introduced for evaluating the significance of subsets of attributes in incomplete decision tables. Some properties of rough conditional entropy are derived. And three attribute reduction algorithms are provided, including an algorithm using exhaustive search, an algorithm using heuristic search and an algorithm using probabilistic search for incomplete decision tables. Furthermore, several simulation experiments on real incomplete data sets are carried out to assess the efficiency of the proposed algorithms. The final simulation results indicate that two of above algorithms can give satisfying performances in the procedure of attribute reduction for incomplete decision tables.