A new classification method using soft decision-making based on an aggregation operator of fuzzy parameterized fuzzy soft matrices
Year: 2021
Type: article
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.
Authors: SAMET MEMİŞ, SERDAR ENGİNOĞLU, UĞUR ERKAN
Language: English
Topic: Fuzzy and Soft Set Theory
Field: Decision Sciences
Domain: Social Sciences
Open Access status: diamond
Awards: G2138603981, G3582819162
OpenAlex ID: W4205642300