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”.
Highlights
Soft sets (Molodtsov, 1999), a standard and practical mathematical tool, are often used for modelling uncertainties, and a great variety of studies have been conducted on this concept (Çağman and Deli, 2012a, b; Deli and Çağman, 2015; Enginoğlu et al, 2015; Şenel, 2016; Zorlutuna and Atmaca, 2016; Atmaca, 2017; Çıtak and Çağman, 2017; Riaz and Hashmi, 2017; Atmaca, 2019; Çıtak, 2018; Riaz and Hashmi, 2018; Riaz et al, 2018; Şenel 2018a, b; Jana et al, 2019; Karaaslan, 2019a, b; Sezgin et al, 2019a, b)
The results show that proposed method performs better than Fuzzy Soft Set Classifier (FSSC), FussCyier, HDFSSC, and Fuzzy kNN for “Breast Cancer Wisconsin (Diagnostic)”, “Immunotherapy”, “Pima Indian Diabetes”, and “Statlog Heart”
We have proposed the classification method Fuzzy Parameterized Fuzzy Soft Normalized Hamming Classifier (FPFSNHC) based on normalised Hamming pseudo-similarity of fpfs-matrices
Summary
Soft sets (Molodtsov, 1999), a standard and practical mathematical tool, are often used for modelling uncertainties, and a great variety of studies have been conducted on this concept (Çağman and Deli, 2012a, b; Deli and Çağman, 2015; Enginoğlu et al, 2015; Şenel, 2016; Zorlutuna and Atmaca, 2016; Atmaca, 2017; Çıtak and Çağman, 2017; Riaz and Hashmi, 2017; Atmaca, 2019; Çıtak, 2018; Riaz and Hashmi, 2018; Riaz et al, 2018; Şenel 2018a, b; Jana et al, 2019; Karaaslan, 2019a, b; Sezgin et al, 2019a, b). Studies on the matrix representations of these sets have been increasingly continued such as soft matrices (Çağman and Enginoğlu, 2010), fuzzy soft. The rest of the paper is organised as follows: In Section 2, we present definitions of fpfs-sets (Çağman et al, 2010; Enginoğlu, 2012), fpfsmatrices (Enginoğlu, 2012; Enginoğlu and Çağman, In Press), and normalised Hamming pseudo-similarity of fpfs-matrices. A Data Classification Method in Machine Learning Based on Normalised Hamming Pseudo-Similarity of Fuzzy Parameterized Fuzzy Soft Matrices. This study is a part of the first author’s PhD dissertation
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