A novel distance measure over intuitionistic fuzzy sets with its applications in Pattern Recognition
| Autor: | Rinki Solanki, Pranab K. Muhuri, Meenakshi Kaushal, MD.Mustafizur Rahman, Q.M.D. Lohani |
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| Rok vydání: | 2018 |
| Předmět: |
Computer science
business.industry Fuzzy set Intuitionistic fuzzy Pattern recognition 02 engineering and technology 01 natural sciences Measure (mathematics) Distance measures 010305 fluids & plasmas Euclidean distance 0103 physical sciences Pattern recognition (psychology) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Value (mathematics) |
| Zdroj: | SSCI |
| Popis: | In this paper, we propose a novel distance measure that relies on noble idea of implementing probability over IFSs. The probability for membership value, non-membership value and hesitation part could be appropriately determined through the methodology provided in this paper. Our approach of defining distance measure through IFSs is novel in the sense that it takes into account all the components of IFSs i.e. membership value, non-membership value and hesitation part. Finally, several real-life pattern recognition problems are considered to show that the proposed IFSs based distance measure outperforms most of the existing distance measures. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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