A High-Dimensional Modeling System Based on Analytical Hierarchy Process and Information Criteria

Abstract

High-dimensional data sets frequently occur in several scientific areas, and special techniques are required to analyze these types of data sets. Especially, it becomes important to apply a suitable model in classification problems. In this study, a novel approach is proposed to estimate a statistical model for high-dimensional data sets. The proposed method uses analytical hierarchical process (AHP) and information criteria for determining the optimal PCs for the classification model. The high-dimensional colon and gravier datasets were used in evaluation part. Application results demonstrate that the proposed approach can be successfully used for modeling purposes.