Investigating composite neighbourhood structure for attribute reduction in rough set theory

Attribute reduction is one of the main issues in the theoretical research of rough set theory which is known as a NP-hard optimization problem. The objective is to find the minimal number of attributes from a large dataset. Hence it is difficult to solve to optimality. This paper proposes a composite neighbourhood structure approach to solve the attribute reduction problem that consists of two versions. The first version is a basic composite neighbourhood structure (CNS) approach where the neighbourhood is selected at random. For the second version, the selection of the neighbourhood structure is based on certain rules (coded as IS-CNS). Both of the algorithms only accept an improved solution. The proposed approach is tested on a set of benchmark datasets taken from University of California, Irvine (UCI) machine learning respiratory in comparison with a set of state-of-the-art methods from the literature. The experimental results show that the proposed approach is able to produce competitive results for the test datasets.