Rough-set-based reduction technique for case attributes
基于粗糙集的案例属性约简技术

To improve the efficiency of case retrieving in CBR(case-based reasoning),the rough-set theory is introduced in this paper to study the reduction technique for case attributes.Firstly,the concept of quasi-reduction is presented.The necessary and sufficient conditions for some attribute-set to become quasi-reduction,and the quasi-reduction to become reduction are then proved.Secondly,starting from the core,a differentiating matrix-based improved algorithm for minimal attribute reduction is then proposed.To maintain its application to continuous attributes,the dispersing algorithm based on the sensitivity of approximation precision is also proposed.Finally,the technique is applied to a practical dynamic scheduling problem of an iron and steel works.The computation experiment shows that it eliminates redundant information, improving the efficiency of case retrieving.