A Rough Hypercuboid Approach for Feature Selection in Approximation Spaces

A Novel Variable Precision Reduction Approach to Comprehensive Knowledge Systems

“…Although the attributes are the same with the exception of c 6 , they have different attribute ranks. They provide dynamic opportunities to join the candidate reduct(s), which is obviously different from the importance of attributes in the model [49]. This is a static quantity and only equivalent to our attribute ranks in the ancestor branch in Figure 4.…”

Rough hypercuboid based supervised clustering of miRNAs

“…62,64 The concept of rough hypercuboid was initially introduced in ref. 64 and 66 and it was successfully used in ref. 62.…”

“…Hence, the degree of dependency of decision attribute on a condition attribute set is evaluated by finding the implicit hypercuboids that encompass misclassified objects. Using the concept of hypercuboid equivalence partition matrix, the misclassified objects of implicit hypercuboids can be identified based on the confusion vector defined next 62,64 …”

“…The significance of the attribute M k with respect to the condition attribute set {M k ,M l } is given by 62,64 …”

“…In this regard, this paper presents a new rough hypercuboid based supervised clustering algorithm. It is developed by integrating the concepts of rough hypercuboid equivalence partition matrix 62,64 and supervised attribute clustering algorithm. 55 It finds coregulated clusters of miRNAs whose collective expression is strongly associated with the sample categories.…”