基于邻域粒化和粗糙逼近的数值属性约简

对于空间中的任一子集,通过基本邻域信息粒子进行逼近,由此提出了邻域信息系统和邻域决策表模型.分析了该模型的性质,并且基于此模型构造了数值型属性的选择算法.利用UCI标准数据集与现有算法进行了比较分析,实验结果表明,该模型可以选择较少的特征而保持或改善分类能力.

Numerical Attribute Reduction Based on Neighborhood Granulation and Rough Approximation

To deal with numerical features, a neighborhood rough set model is proposed based on the definitions of ( neighborhood and neighborhood relations in metric spaces. Each object in the universe is assigned with a neighborhood subset, called neighborhood granule. The family of neighborhood granules forms a concept system to approximate an arbitrary subset in the universe with two unions of neighborhood granules: lower approximation and upper approximation. Thereby, the concepts of neighborhood information systems and neighborhood decision tables are introduced. The properties of the model are discussed. Furthermore, the dependency function is used to evaluate the significance of numerical attributes and a forward greedy numerical attribute reduction algorithm is constructed. Experimental results with UCI data sets show that the neighborhood model can select a few attributes but keep, even improve classification power.