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”.
|Konular||Mühendislik, Ortak Disiplinler|
|Teşekkür||The authors thank Dr Uğur Erkan for technical support.|
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|