| 胡清华,于达仁,谢宗霞.基于邻域粒化和粗糙逼近的数值属性约简.软件学报,2008,19(3):640-649 |
| 基于邻域粒化和粗糙逼近的数值属性约简 |
| Numerical Attribute Reduction Based on Neighborhood Granulation and Rough Approximation |
| 投稿时间：2006-09-18 修订日期：2006-11-27 |
| DOI： |
| 中文关键词: 数值特征 粒度计算 邻域关系 粗糙集 可变精度 属性约简 特征选择 |
| 英文关键词:numerical feature granular computing neighborhood relation rough set variable precision attribute reduction feature selection |
| 基金项目:Supported by the National Natural Science Foundation of China under Grant No.60703013(国家自然科学基金);the Development Program for Outstanding Young Teachers in Harbin Institute of Technology of China under Grant HITQNJS.2007.017(哈尔滨工业大学优秀青年教师培养计划);the Scientific Research Foundation of Harbin Institute Technology of China under Grant No.HIT2003.35(哈尔滨工业大学校基金) |
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| 中文摘要: |
| 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. |
| 英文摘要: |
| 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. |
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