A new classification method using soft decision-making based on an aggregation operator of fuzzy parameterized fuzzy soft matrices

Recently, 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, decision trees. In this paper, we offer new algorithm called fuzzy parameterized soft aggregation classifier (FPFS-AC) combine modelling ability decision-making (SDM) success eigenvalues. FPFS-AC constructs matrix employing similarity measures matrices fpfs -matrices) eigenvalue-based measure. Then, it applies an SDM based on operator -matrices classifies given test sample. Afterwards, perform experimental study 15 UCI datasets manifest our approach compare with well-known state-of-the-art classifiers (kNN, SVM, kNN, EigenClass, BM-fuzzy kNN) terms accuracy, precision, recall, macro F-score, micro running time. Moreover, statistically analyse experimentally obtained data. Experimental statistical results show that outperforms all concerning five performance metrics.