Rule induction for hierarchical attributes using a rough set for the selection of a green fleet

Develop a solution approach to resolve the hierarchical rough set problem.Explore the most specific decision attribute level by level in the level-search procedure.Apply the proposed approach to the case in the transportation industry to select green fleets.This corporation reduces pollution emissions over the long-term by choosing the green vehicles in a desired ratio. Rough set theory (RST) has been the subject of much study and numerous applications in many areas. However, most previous studies on rough sets have focused on finding rules where the decision attribute has a flat, rather than hierarchical structure. In practical applications, attributes are often organized hierarchically to represent general/specific meanings. This paper (1) determines the optimal decision attribute in a hierarchical level-search procedure, level by level, (2) merges the two stages, generating reducts and inducting decision rules, into a one-shot solution that reduces the need for memory space and the computational complexity and (3) uses a revised strength index to identify meaningful reducts and to improve their accuracy. The selection of a green fleet is used to validate the superiority of the proposed approach and its potential benefits to a decision-making process for transportation industry.