A Framework for the Evaluation of Machine Learning Algorithms based on Fuzzy AHP and VIKOR
Authors: Maikel Yelandi Leyva Vazquez, Francisco Javier Poma Torres, Galo Enrique Valverde Landivar, Miguel Angel Quiroz Martinez
Abstract: Decision-makers consider various criteria, sub-criteria, alternatives, scores, and other parameters for choosing a tool. The problem is decision-making considering objectives that conflict with complex factors such as social, economic, political, technological, or environmental. It is not possible to make decisions based on a single criterion. The objective is to develop a framework for evaluating decisions based on fuzzy logic by hybridizing the fuzzy-AHP and VIKOR methods to select ML algorithms that increase these algorithms' effectiveness in specific problems. The methodology used in this proposal is oriented to exploratory research that allows to identify and define a problem or issue with a quantitative approach. This research resulted in a Decision Assessment Framework based on fuzzy logic and hybridization of methods AHP and VIKOR to select ML algorithms and validate the model obtained through the methods defined in the research. It was concluded that the proposed framework is a technique for determining the detailed evaluation of ML algorithms and resolving multicriteria decision-making; this approach is a tool that uses Fuzzy AHP in assigning weight to criteria, and these scores are processed with VIKOR in a multicriteria mode, and get an alternative/compromise solution, here the evaluation is scientific, fair, and rational.
Keywords: Exemplary Paper, Human Systems Integration, Systems Engineering, Systems Modeling Language
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