A Data Classification Method in Machine Learning Based on Normalised Hamming Pseudo-Similarity of Fuzzy Parameterized Fuzzy Soft Matrices

Year 2019, , 1 - 8, 31.12.2019

Samet Memiş , Serdar Enginoğlu , Uğur Erkan

https://doi.org/10.30516/bilgesci.643821

Cited By: 10

Abstract

In this study, we
propose a classification method based on normalised Hamming pseudo-similarity
of fuzzy parameterized fuzzy soft matrices (fpfs-matrices). We then
compare the proposed method with Fuzzy Soft Set Classifier (FSSC), FussCyier,
Fuzzy Soft Set Classification Using Hamming Distance (HDFSSC), and Fuzzy
k-Nearest Neighbor (Fuzzy kNN) in terms of the performance criterions
(accuracy, precision, recall, and F-measure) and running time by using four
medical data sets in the UCI machine learning repository. The results show that
the proposed method performs better than FSSC, FussCyier, HDFSSC, and Fuzzy kNN
for “Breast Cancer Wisconsin (Diagnostic)”, “Immunotherapy”, “Pima Indian
Diabetes”, and “Statlog Heart”.

Keywords

Fuzzy Sets, Soft Sets, fpfs-Matrices, Similarity Measure, Data Classification

Thanks

The authors thank Dr Uğur Erkan for technical support.

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