BISGA: Recalculating the Entire Boolean-Valued Information System from Aggregates Using a Genetic Algorithm

Abstract

A Boolean-valued information system (BIS) is an application of a soft set in which the data are mapped in a binary form and used in making applications not limited to decision-making, medical diagnoses, game theory, and economics. BIS may be lost for several reasons including virus attacks, improper entry, and machine errors. A concept was presented that the entire lost BIS can be regenerated from four aggregate sets through supposition. Based on that concept, this paper presents an algorithm to recalculate the entire BIS through a genetic algorithm (GA), named BISGA which is more general and easy to implement than the supposition method. A solved example is presented which explains how BISGA works. Furthermore, BISGA is implemented in Python and evaluated on both UCI benchmark datasets and randomized datasets for checking its efficiency and accuracy. Results show that the lost BIS is recovered significantly and accurately; however, the efficiency drops when the size of the BIS increases. This novel approach may help practitioners recalculate the entire lost BIS, which in turn helps in the decision-making process and conclusions.