Quick attribute reduction with generalized indiscernibility models

The efficiency of attribute reduction is one of the important challenges being faced in the field of Big Data processing. Although many quick attribute reduction algorithms have been proposed. they are tightly coupled with their corresponding indiscernibility relations, and it is difficult to extend specific acceleration policies to other reduction models. In this paper, we propose a generalized indiscernibility reduction model(GIRM) and a concept of the granular structure in GIRM, which is a quantitative measurement induced from multiple indiscernibility relations and which can be used to represent the computation cost of varied models. Then, we prove that our GIRM is compatible with three typical reduction models. Based on the proposed GIRM, we present a generalized attribute reduction algorithm and a generalized positive region computing algorithm. We perform a quantitative analysis of the computation complexities of two algorithms using the granular structure. For the generalized attribute reduction, we present systematic acceleration policies that can reduce the computational domain and optimize the computation of the positive region. Based on the granular structure, we propose acceleration policies for the computation of the generalized positive region, and we also propose fast positive region computation approaches for three typical reduction models. Experimental results for various datasets prove the efficiency of our acceleration policies in those three typical reduction models. (C) 2017 Elsevier Inc. All rights reserved.