A new similarity measure of IFSs and its applications
| Autor: | Tran Due Quynh, Nguyen Quang Thuan, Nguyen Xuan Thao, Neuven Van Dinh |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Class (set theory)
Computer science business.industry Sample (statistics) Similarity measure Multiple-criteria decision analysis Machine learning computer.software_genre Measure (mathematics) Ranking (information retrieval) Similarity (network science) Simple (abstract algebra) Artificial intelligence business computer |
| Zdroj: | KSE |
| Popis: | Similarity measures between Intuitionistic fuzzy sets (IFSs) play an important role and they have many applications in machine learning and multi-criteria decision making. However, there are few existing similarity measures. In this paper, we propose a new similarity measure between Intuitionistic fuzzy sets (IFSs). We first present a new mathematical formula and then prove that it satisfies all the conditions of similarity measures. The usefulness of the new similarity measure is pointed out by considering a simple classification problem. The results show that the proposed measure can be used to predict the class of a new sample while some of other measures cannot do it. Next, we apply the new similarity measure for solving multi criteria decision making (MCDM) problems. The results are compared with the ones by using some other similarity measures. The experimentation reports that the new similarity measure may provide different ranking of alternatives but it provides the same optimal solution. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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