Feature Reduct of Decision Tables Based on Feature Matrix
基于特征矩阵的决策表约简研究

Feature reduct of decision tables is important for rough analysis. To consistent decision tables, the minimal reduct has been proved to be NP-hard. Many heuristic algorithms, therefore, have been given but most of them depend on the core of decision tables, which is not easy to get, especially for large-scale decision tables. In this way, there exist some problems such as efficiency and the solution completeness in them. Based on feature matrix put forward for reduct of decision tables, a new method to solve the difficulty is proposed in this paper. The method not only is independent of the core of decision tables, but provides a way-out for it. Also, decision tables containing null values, which are regardless and hard to deal with at present, are analyzed in depth in order to find useful information.