Memis, SametEnginoglu, SerdarErkan, Ugur2025-01-272025-01-2720221300-06321303-6203https://doi.org/10.55730/1300-0632.3816https://hdl.handle.net/20.500.12428/26538Recently, a precise and stable machine learning algorithm, i.e. eigenvalue classification method (EigenClass), has been developed by using the concept of generalised eigenvalues in contrast to common approaches, such as k-nearest neighbours, support vector machines, and decision trees. In this paper, we offer a new classification algorithm called fuzzy parameterized fuzzy soft aggregation classifier (FPFS-AC) to combine the modelling ability of soft decision-making (SDM) and classification success of generalised eigenvalues. FPFS-AC constructs a decision matrix by employing the similarity measures of fuzzy parameterized fuzzy soft matrices (fpfs-matrices) and a generalised eigenvalue-based similarity measure. Then, it applies an SDM method based on the aggregation operator of fpfs-matrices to a decision matrix and classifies the given test sample. Afterwards, we perform an experimental study using 15 UCI datasets to manifest the success of our approach and compare FPFS-AC with the well-known and state-of-the-art classifiers (kNN, SVM, fuzzy kNN, EigenClass, and BM-fuzzy kNN) in terms of accuracy, precision, recall, macro F-score, micro F-score, and running time. Moreover, we statistically analyse the experimentally obtained data. Experimental and statistical results show that FPFS-AC outperforms the state-of-the-art classifiers in all the datasets concerning the five performance metrics.eninfo:eu-repo/semantics/closedAccessFuzzy setssoft setssoft decision-making (SDM)fpfs -matricessupervised learningdata classificationArticle30387189010.55730/1300-0632.3816Q4WOS:0007745998000242-s2.0-85128321544Q2