SIMILARITY-BASED ROUGH SET APPROACH IN INCOMPLETE INFORMATION SYSTEM USING POSSIBLE EQUIVALENT VALUE-SET

Abstract

Data analytics generally helps businesses or entities to make better and efficient decision making. But in the face of growing volume of data or information, it becomes challenging to achieve these goals. One of which is on classification of information with high accuracy. Furthermore, when the information is incomplete, definitely it is more challenging in order to classify the information with high accuracy. Although incomplete information is well discussed using rough set theory for data classification, such as based on tolerance and similarity relations, there are still issues on accuracy to evaluate data classification. The main objective of this paper is to introduce a new similarity approach with semantically justified based on possible equivalent value-set related to incomplete information systems. It is based on a classification of three semantics types of incomplete information i.e., “any value”, “maybe value” and “not applicable value” for modelling similarity. Subsequently, the similarity precision between objects in incomplete information systems is considered. The comparative studies and simulation results between the proposed approach in terms of accuracy on synthetic data, four well-known classification datasets and one real marine dataset are presented. The proposed approach improves the accuracy up to two orders of magnitude and, thus verifying its data classification accuracy.