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),\rhas been developed by using the concept of generalised eigenvalues in contrast to common approaches, such as k-nearest\rneighbours, support vector machines, and decision trees. In this paper, we offer a new classification algorithm called fuzzy\rparameterized fuzzy soft aggregation classifier (FPFS-AC) to combine the modelling ability of soft decision-making (SDM)\rand classification success of generalised eigenvalues. FPFS-AC constructs a decision matrix by employing the similarity\rmeasures of fuzzy parameterized fuzzy soft matrices (fpfs-matrices) and a generalised eigenvalue-based similarity measure.\rThen, it applies an SDM method based on the aggregation operator of fpfs-matrices to a decision matrix and classifies\rthe given test sample. Afterwards, we perform an experimental study using 15 UCI datasets to manifest the success\rof our approach and compare FPFS-AC with the well-known and state-of-the-art classifiers (kNN, SVM, fuzzy kNN,\rEigenClass, and BM-fuzzy kNN) in terms of accuracy, precision, recall, macro F-score, micro F-score, and running time.\rMoreover, we statistically analyse the experimentally obtained data. Experimental and statistical results show that\rFPFS-AC outperforms the state-of-the-art classifiers in all the datasets concerning the five performance metrics.