Conceptual Multi-Level Hierarchy for Evaluation and Classification

Abstract

Conceptual hierarchy represents the relationship between objects or concepts in a hierarchical form. The work presented here focuses on creating hierarchies which have a relationship between parent and child nodes but not between siblings. When we evaluate or classify certain objects (e.g., service quality), we often use a conceptual hierarchy which has various items (concept) at its nodes. If the target of the evaluation/classification has more complicated features, we need a more complicated conceptual hierarchy. And currently most conceptual hierarchies are constructed qualitatively. This paper presents two new quantitative approaches in constructing a conceptual multi-level hierarchy. They are novel in the sense that a multi-level hierarchy (conceptual relationship knowledge) can be generated from a set of questionnaire survey data by applying factor analysis, structural equation modeling, and decision tree induction (C5.0) techniques. Through this factor analysis we discovered pattern knowledge from the heap of questionnaire survey data which contained hidden knowledge about the problem domain. It is a fresh idea to consider the questionnaire survey data as another form of knowledge elicitation for pattern discovery. AHP (Analytic Hierarchy Process) is a widely used technique for multi-criteria decision making. The significance of this paper is that it can substitute the qualitative stage of building hierarchy in the AHP technique, which is renowned for its weakness in hierarchy building.