Rahmat, Hidayat and Iwan Tri Riyadi, Yanto and Azizul Azhar, Ramli and Mohd Farhan Md., Fudzee and Ansari Saleh, Ahmar (2021) Generalized Normalized Euclidean Distance Based Fuzzy Soft Set Similarity for Data Classification. Computer Systems Science & Engineering, 38 (1).
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Abstract
Classification is one of the data mining processes used to predict predetermined target classes with data learning accurately. This study discusses data classification using a fuzzy soft set method to predict target classes accurately. This study aims to form a data classification algorithm using the fuzzy soft set method. In this study, the fuzzy soft set was calculated based on the normalized Hamming distance. Each parameter in this method is mapped to a power set from a subset of the fuzzy set using a fuzzy approximation function. In the classification step, a generalized normalized Euclidean distance is used to determine the similarity between two sets of fuzzy soft sets. The experiments used the University of California (UCI) Machine Learning dataset to assess the accuracy of the proposed data classification method. The dataset samples were divided into training (75% of samples) and test (25% of samples) sets. Experiments were performed in MATLAB R2010a software. The experiments showed that: (1) The fastest sequence is matching function, distance measure, similarity, normalized Euclidean distance, (2) the proposed approach can improve accuracy and recall by up to 10.3436% and 6.9723%, respectively, compared with baseline techniques. Hence, the fuzzy soft set method is appropriate for classifying data.
| Item Type: | Article |
|---|---|
| Subjects: | KARYA ILMIAH DOSEN Universitas Negeri Makassar > KARYA ILMIAH DOSEN |
| Divisions: | KOLEKSI KARYA ILMIAH UPT PERPUSTAKAAN UNM MENURUT FAKULTAS > KARYA ILMIAH DOSEN KARYA ILMIAH DOSEN FAKULTAS MIPA |
| Depositing User: | Ansari Saleh Ahmar |
| Date Deposited: | 15 Mar 2023 11:47 |
| Last Modified: | 19 Jun 2023 03:17 |
| URI: | http://eprints.unm.ac.id/id/eprint/27396 |
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