A New Classification Method Using Soft Decision-Making Based on an Aggregation Operator of Fuzzy Parameterized Fuzzy Soft Matrices
Citation: S. Memiş, S. Enginoğlu, and U. Erkan, 2022. A New Classification Method Using Soft Decision-Making Based on an Aggregation Operator of Fuzzy Parameterized Fuzzy Soft Matrices, Turkish Journal of Electrical Engineering and Computer Sciences, 30(3), 1165–1180. doi: https://doi.org/10.55730/1300-0632.3816
Abstract:
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, 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.