Lossless Pruning of AHP SNP Prioritizaton Tree Using Random Forest Variable Importances

2017-06-30
Yılmaz, Arif
Aydın Son, Yeşim
Subjectivity is an old yet unsolved problem in Multiple Criteria Decision Making including Analytic Hierarchy Processing (AHP). Here, we have proposed a machine learning based analytic hierarchy process (ML-AHP) method to address expert judgment uncertainty in decision making system design. It is accomplished by training a classifier algorithm according to Analytic Hierarchy Process input data and evaluating categories based on variable importance values. As a comparative case study Single Nucleotide Polymorphism(SNP) prioritization in the bioinformatics domain is presented. Variable Importance figures provided by the employed machine learner are used to evaluate the importanceof AHP categories. In this study we selected Random Forest. It was discovered that most of the expert defined weights of categories were zero and performance was identical after pruning. Analysis on the Prostate Cancer and the Type 2 Diabetes Mellitus disease data were performed to demonstrate the benefits of the proposed approach, where pairwise comparisons of categories required no expert evaluation. Hence, subjectivity, uncertainty and imprecision is avoided. Implementation of the proposed method can enhance evaluation of the category weights in AHP and also in other multiple criteria decision making methods

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Citation Formats
A. Yılmaz and Y. Aydın Son, “Lossless Pruning of AHP SNP Prioritizaton Tree Using Random Forest Variable Importances,” presented at the 10th International Symposium on Health Informatics and Bioinformatics, Güzelyurt, Kıbrıs, 2017, Accessed: 00, 2021. [Online]. Available: http://hibit2017.ii.metu.edu.tr/wordpress/wp-content/uploads/HIBIT2017_Conference_Book.pdf.