UPSI Digital Repository (UDRep)
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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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