基于矩阵策略的不完备混合型数据增量式特征选择算法

Incremental Feature Selection Algorithm for Incomplete Mixed Data Based on Matrix Strategy

特征选择是粗糙集理论在数据挖掘等领域中一种重要的应用,如何对动态变化的信息系统进行增量式特征选择是目前粗糙集理论研究的重点。在不完备混合型信息系统中,属性集的不断增加是信息系统动态变化的一种重要形式。首先在不完备混合型信息系统中引入邻域条件熵的概念,并且利用矩阵的方法去表示邻域条件熵;然后针对属性集动态增加的情形,提出矩阵形式的邻域条件熵增量式更新,并且基于这种增量式更新机制给出了相应的增量式特征选择算法;最后,UCI数据集的实验结果表明,所提出的增量式特征选择算法比非增量式特征选择算法具有更高的特征选择性能。

Feature selection is an important application of rough set theory in data mining and other fields.How to make incremental feature selection for dynamic information systems is the focus of rough set theory research at present.In incomplete mixed information system,the increasing attribute set is an important form of dynamic change of information system.First,the concept of neighborhood conditional entropy is introduced into incomplete mixed information system,and is represented by matrix method.Then,in view of the dynamic increase of attribute set,an incremental updating method based on matrix form of neighborhood conditional entropy is proposed,and an incremental feature selection algorithm is given based on this incremental updating mechanism.Finally,the experimental results on UCI datasets show that the proposed incremental feature selection algorithm has higher feature selection performance than the non-incremental feature selection algorithm does.