Yıl 2019, Cilt 3 , Sayı 0, Sayfalar 1 - 8 2019-12-31

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

Samet MEMİŞ [1] , Serdar ENGİNOĞLU [2] , Uğur ERKAN [3]


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”.

Fuzzy Sets, Soft Sets, fpfs-Matrices, Similarity Measure, Data Classification
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Birincil Dil en
Konular Mühendislik, Ortak Disiplinler
Bölüm Araştırma Makaleleri
Yazarlar

Orcid: 0000-0002-0958-5872
Yazar: Samet MEMİŞ (Sorumlu Yazar)
Kurum: CANAKKALE ONSEKIZ MART UNIVERSITY
Ülke: Turkey


Orcid: 0000-0002-7188-9893
Yazar: Serdar ENGİNOĞLU
Kurum: CANAKKALE ONSEKIZ MART UNIVERSITY
Ülke: Turkey


Yazar: Uğur ERKAN
Kurum: KARAMANOGLU MEHMETBEY UNIVERSITY
Ülke: Turkey


Teşekkür The authors thank Dr Uğur Erkan for technical support.
Tarihler

Yayımlanma Tarihi : 31 Aralık 2019

APA Memi̇ş, S , Engi̇noğlu, S , Erkan, U . (2019). A Data Classification Method in Machine Learning Based on Normalised Hamming Pseudo-Similarity of Fuzzy Parameterized Fuzzy Soft Matrices . Bilge International Journal of Science and Technology Research , ICONST 2019 , 1-8 . DOI: 10.30516/bilgesci.643821


CONFIGURATIONS OF SEVERAL SOFT DECISION-MAKING METHODS TO OPERATE IN FUZZY PARAMETERIZED FUZZY SOFT MATRICES SPACE
Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering
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https://doi.org/10.18038/estubtda.562578