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

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 statistic...

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Bibliographic Details
Published inMathematical problems in engineering Vol. 2021; pp. 1 - 9
Main Author Koç, Tuba
Format Journal Article
LanguageEnglish
Published New York Hindawi 2021
Hindawi Limited
Subjects
Algorithms
Alzheimer's disease
Analytic hierarchy process
Classification
Colon
Criteria
Datasets
Eigenvalues
Gene expression
Mathematical problems
Neural networks
Principal components analysis
Sample variance
Statistical models
Variables
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Summary: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.
ISSN:1024-123X
1563-5147
DOI:10.1155/2021/6198317