Fuzzy similarity-based rough set method for case-based reasoning and its application in tool selection

Abstract Case-based reasoning (CBR) embodied in die and mold NC machining will extend the application of knowledge-based system by utilizing previous cases and experience. However, redundant features may not only dramatically increase the case memory, but also make the case retrieval algorithm more complicated. Additionally, traditional methods of feature weighting limit the development of CBR methodology. This paper presents a novel methodology to apply fuzzy similarity-based Rough Set algorithm in feature weighting and reduction for CBR system. The algorithm is used in tool selection for die and mold NC machining. The proposed method does not need to discretize continuous or real-valued features included in cases, from which can effectively reduce information loss. The weight of feature ai is computed based on the difference of its dependency defined as γ A − γ A − { a i } , which also represents the significance of the corresponding feature. If the difference is equal to 0, the feature is considered to be redundant and should be removed. Finally, a case study is also implemented to prove the proposed method.