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Type :thesis
Subject :RA Public aspects of medicine
Main Author :Ali, Mohammed Assim Mohammed
Title :Benchmarking methodology for multiclass classification models based on multi criteria decision analysis
Place of Production :Tanjong Malim
Publisher :Fakulti Seni, Komputeran dan Industri Kreatif
Year of Publication :2019
Corporate Name :Universiti Pendidikan Sultan Idris
PDF Guest :Click to view PDF file

Abstract : Universiti Pendidikan Sultan Idris
The purpose of this research was to develop a benchmarking methodology for aiding medical organizations’ administrations in benchmarking and ranking available multiclass classification models to select the best one. Medical organizations have been facing difficulties in evaluating and comparing classification models. Experimental and case study research methods were adopted in this study. The new benchmarking methodology was proposed based on two stages. In first stage, a Decision Matrix (DM) was constructed based on the crossover of two groups of multi-evaluation criteria and 22 multiclass classification models. The matrix was evaluated using secondary datasets consisting of 72 samples of acute leukemia, including 5327 gens. In the second stage, multi-criteria decision-making techniques, namely, Best and Worst method (BWM) and Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) were used to benchmark and ranked the multiclass classification models. The BWM was applied to calculate the weights of evaluation criteria, whereas VIKOR was used to benchmark and rank the multi-class classification models. VIKOR was utilized in two decision-making contexts, namely individual and group contexts. In group decision making, internal and external group aggregations are applied. For validating the proposed methodology, an objective method was used. The results showed that (1) the integration of BWM and VIKOR was effective for solving the benchmarking/selection problems of multi-class classification models. (2) The ranks of multi-class classification models obtained from internal and external VIKOR group decision making were almost the same, where, Bayes. Naïve Byes Updateable, Bayes Net, Decision Stump were the first three classification models respectively and Trees. LMT was the last one. (3) In the objective validation, the ranking results of internal and external VIKOR group decision making were valid. Clearly, as a conclusion, the proposed methodology can be used for evaluation and benchmarking different multiclass classification models for various applications. The implications of this study will benefit medical organizations by enabling them to make the right decisions regarding the use of multi-class classification models for acute leukemia and the implications also benefit medical classification software developers who work in industrial companies and institutions in developing classification models.

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