Taxonomy of rough set approaches for rule generation

The rough set method is one of the well-known methods for classifying an object. The rough set method has been used since Pawlak discovered it in 1982. The rough set method can analyze data related to redundant attribute reduction, find the most significant attributes and make decision rules. This article presents a taxonomy of rough set approaches for generating rules. The rough set method in generating rules has been successfully applied in various fields, such as classification, decision analysis of medical data, manufacturing processes, machine operations, predictive data, evaluation of television sets, iris fisher data, trading stock indexes, information systems, learning database systems, scientific diagnostics, force protection, KANSEI Engineering, prediction, customization provider strategy, force stabilizer system, scientific data and vehicle traffic. Several researchers since 2000 have combined the rough set method with other methods or algorithms to get fewer rules and not reduce classification accuracy. The results of combining these methods have been shown to improve the performance of the rough set method.