A parallel implementation of computing composite rough set approximations on GPUs

Abstract

In information systems, there may exist multiple different types of attributes like categorical attributes, numerical attributes, set-valued attributes, interval-valued attributes, missing attributes, etc. Such information systems are called as composite information systems. To process such attributes with rough set theory, composite rough set model and corresponding matrix methods were introduced in our previous research. Rough set approximations of a concept are the basis for rule acquisition and attribute reduction in rough set based methods. To accelerate the computation process of rough set approximations, this paper first presents the boolean matrix representation of the lower and upper approximations in the composite information system, then designs a parallel method based on matrix, and implements it on GPUs. The experiments on data sets from UCI and user-defined data sets show that the proposed method can accelerate the computation process efficiently. © 2013 Springer-Verlag.