A comparative study of multi-objective pso- fuzzy optimization with weka classification algorithms to improve the interpretability and accuracy in medical data - Batoul Rashed A , Alwatben; Alhujaylan , Abeer (International Journal of Engineering and Technology, 2024)
Designing an Automatic Fuzzy Rule-based Classification Systems (FRBCSs) is considered as optimization problem associated to a result of high interpretability and accuracy. Interpretability and accuracy are the two main objectives to be improved in the optimization measurement of FRBCSs. However, improving these objectives is found to be difficult in most of the existing systems due to the conflicting issues between accuracy and interpretability. Evolutionary algorithms have been extensively used to resolve problems associated with multiple and often conflicting objectives. The objective of a multi- objective optimization algorithm is to define the collection of best trade- offs between objectives. Among multi-objective evolutionary algorithms proposed in the literature, particle swarm optimization (PSO)-based multi-objective (MOPSO) algorithm has been cited to be the most representative. This paper presents a comparative analysis of Machine Learning (ML) algorithms for Fuzzy Multi-objective evolutionary algorithms (FMOEs) with WEKA Classification algorithms of data using8 publicly available datasets from the Knowledge Extraction based on Evolutionary Learning (KEEL) repository. FMOEs method is evaluated on a popular benchmark data set being used in a medical domain for evaluations. The result obtained shows that the FMOEs method yields an optimum solution in minimizing the trade-off between accuracy and interpretability. These findings add to a better understanding of the trade-offs between accuracy and interpretability in the algorithms under consideration, assisting academics and practitioners in picking the best algorithm for classification tasks.