The Improved Similarity Measures of Vague Sets under the Influence of the Waiver
Feng Weibing, Guo Luna, Wang Pan
Available Online January 2017.
- https://doi.org/10.2991/iconfem-16.2016.44How to use a DOI?
- similarity measures; Vague sets; the impact of waiver; pattern recognition
- The similarity measure of vague sets is an important content for studying. A reasonable structure and the way to select a similarity measure will directly affect the classification of large data and the correct judgment of the data relationships. Based on the comparison and analysis of the existing similarity measures, and aiming at the shortcomings that it does not take into the abstained part to the impact of the similarity measures and cannot effectively distinguish the data in some existing similarity measures, this paper investigates the similarity measure of vague sets (values), proposes new formulas to calculate the similarity measure of vague sets (values) and proves the completeness of the definition in theory. The distinguishability and rationality of the improved similarity measures are also illustrated by experimental data and pattern recognition.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Feng Weibing AU - Guo Luna AU - Wang Pan PY - 2017/01 DA - 2017/01 TI - The Improved Similarity Measures of Vague Sets under the Influence of the Waiver BT - 2016 International Conference on Engineering Management (Iconf-EM 2016) PB - Atlantis Press SP - 33 EP - 39 SN - 2352-5428 UR - https://doi.org/10.2991/iconfem-16.2016.44 DO - https://doi.org/10.2991/iconfem-16.2016.44 ID - Weibing2017/01 ER -