A Numerical Classification Technique Based on Fuzzy Soft Set Using Hamming Distance

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

“…In this paper, we have proposed the classification method FPFSNHC based on normalised Hamming pseudo-similarity of fpfs-matrices. We then compare proposed method with FSSC (Handaga et al 2012), FussCyier (Lashari et al 2017), HDFSSC (Yanto et al, 2018), and Fuzzy kNN (Keller et al 1985) in terms of the performance criterions (accuracy, precision, recall, and Fmeasure) and running times by using four medical data sets in the UCI machine learning repository (Dua and Graff, 2019 Moreover, different classification algorithms also can be developed by using soft decision-making methods via fpfs-matrices such as (Enginoğlu and Memiş, 2018a, b, c, d;Enginoğlu et al, 2018a, b, c, d;Enginoğlu and Çağman, In Press).…”

“…In this paper, we have proposed the classification method FPFSNHC based on normalised Hamming pseudo-similarity of fpfs-matrices. We then compare proposed method with FSSC (Handaga et al 2012), FussCyier (Lashari et al 2017), HDFSSC (Yanto et al, 2018), and Fuzzy kNN (Keller et al 1985) in terms of the performance criterions (accuracy, precision, recall, and Fmeasure) and running times by using four medical data sets in the UCI machine learning repository (Dua and Graff, 2019). In Immunotherapy data set, FPFSNHC (70.67,66.68,73.16,64.63) has advantage over FSSC (62.28,61.15,65.84,56.69),FussCyier (68.00,63.48,68.12,60.99),HDFSSC (67.89,62.98,68.09,60.78),and Fuzzy kNN (61.33,45.04,45.60,43.18) and in Statlog Heart data set, FPFSNHC (83.39,83.79,82.50,82.62) has advantage over FSSC (80.78,81.30,81.61,80.49),FussCyier (82.46,82.54,81.69,81.73),HDFSSC (79.81,79.50,79.36,79.18),and Fuzzy kNN (58.22,57.84,57.70,57.01), concerning accuracy, precision, recall, and F-measure results, respectively.…”

“…In Section 3, we propose Fuzzy Parameterized Fuzzy Soft Normalized Hamming Classifier (FPFSNHC) using normalised Hamming pseudo-similarity of fpfs-matrices. In Section 4, we compare FPFSNHC with Fuzzy Soft Set Classifier (FSSC) (Handaga et al, 2012), FussCyier (Lashari et al, 2017), Fuzzy Soft Set Classification Using Hamming Distance (HDFSSC) (Yanto et al, 2018), and Fuzzy k-Nearest Neighbor (Fuzzy kNN) (Keller et al, 1985) 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 (Dua and Graff, 2019). The results show that proposed method performs better than FSSC, FussCyier, HDFSSC, and Fuzzy kNN for "Breast Cancer Wisconsin (Diagnostic)", "Immunotherapy", "Pima Indian Diabetes", and "Statlog Heart".…”

A Soft Set Approach for Handling Conflict Situation on Movie Selection

“…It is a parametrised family of subsets from a universal set, which proved to have a wide variety of applications [18]- [21]. Several studies have extended its idea to include; description logics [22], soft group [23], operations [24], relations and functions [25], transitive closure and ordering [26], formal relationships with fuzzy [27], fuzzy soft set (FSS) based on Hamming distance [28] and several other extensions [29]- [36].…”

A Relative Tolerance Relation of Rough Set in Incomplete Information

“…In various fields like financial banking, medicine, manufacturing engineering, customer relationship management, web mining, geochemical and e-learning (Saedudin et al 2018(Saedudin et al , 2017(Saedudin et al , 2016Sutoyo et al 2019Sutoyo et al , 2017Yanto et al 2018aYanto et al , 2018bYanto et al , 2016 can apply data mining concepts and methods. The new emerging technique of data mining is educational data mining that can be applied to the data related to the field of education (Romero & Ventura 2007), including students' performance evaluation.…”