FPFS-EC

Numerical Data Classification via Distance-Based Similarity Measures of Fuzzy Parameterized Fuzzy Soft Matrices

FPFS-EC

Numerical Data Classification via Distance-Based Similarity Measures of Fuzzy Parameterized Fuzzy Soft Matrices

Citation: Memiş, S., Enginoğlu, S., Erkan, U., 2021. Numerical Data Classification via Distance-Based Similarity Measures of Fuzzy Parameterized Fuzzy Soft Matrices. IEEE Access, 9, 88583 - 88601. doi: https://doi.org/10.1109/ACCESS.2021.3089849

Abstract:

In this paper, we first define eight pseudo-metrics and eight pseudo-similarities based on these pseudo-metrics over fpfs-matrices. We then propose a new classification algorithm, i.e. Fuzzy Parameterized Fuzzy Soft Euclidean Classifier (FPFS-EC), based on Euclidean pseudo-similarity. After that, we compare FPFS-EC with Support Vector Machines (SVM), Fuzzy k-Nearest Neighbor (Fuzzy kNN), Fuzzy Soft Set Classifier (FSSC), FussCyier, Fuzzy Soft Set Classification Using Hamming Distance (HDFSSC), and Fuzzy kNN Based on the Bonferroni Mean (BM-Fuzzy kNN) in terms of the performance criteria - namely accuracy, precision, recall, macro F-score, and micro F-score - and running time by using 18 real-world datasets in the UCI machine learning repository. The results show that FPFS-EC performs better in the occurrence of the 13 of 18 datasets in question than SVM, Fuzzy kNN, FSSC, FussCyier, HDFSSC, and BM-Fuzzy kNN.