Iterative Hierarchy and Ranking Process (IHRP): A Novel Effective Hierarchy Method for Densely Connected Systems and Case Study in Student Performance Assessment
CoRR(2023)
摘要
In real-life decision-making problems, determining the influences of the
factors on the decision attribute is one of the primary tasks. To affect the
decision attribute most, finding a proper hierarchy among the factors and
determining their importance values in the system becomes quite important.
Interpretive structural modeling (ISM) is a widely used hierarchy-building
method that mines factor inter-influences based on expert opinions. This paper
discusses one of the main drawbacks of the conventional ISM method in systems
where the factors are densely interrelated. We refer to such systems as "dense
systems". We propose a novel iterative hierarchy-building technique, called
'Iterative Hierarchy and Ranking Process'(IHRP) which performs effectively in
such dense systems. To take the vagueness of the expert opinions into account,
intuitionistic fuzzy linguistics has been used in the research work. In this
paper, we propose a two-stage calculation of the relative importance of the
factors in the system based on their hierarchical positions and rank the
factors accordingly. We have performed a case study on student performance
assessment by taking up novel Indian high-school administrative factors' data
collected by surveying the experts in this field. A comparative study has been
conducted in terms of the correlation of the factor ranking achieved by the
proposed method and conventional ISM method with that of standard outranking
methods like TOPSIS, and VIKOR. Our proposed IHRP framework achieves an 85-95
correlation compared to a 50-60
This proves the effectiveness of the proposed method in determining a better
hierarchy than the conventional method, especially in dense systems.
更多查看译文
关键词
ranking process,hierarchy method,densely connected systems,assessment,study
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要