A Simplified Approach for Data Filling in Incomplete Soft Sets
SSRN, ISSN: 1556-5068
2022
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Data analysis is widely used in various fields, and data is often uncertain, which increases the difficulty of solving problems. Soft set method is a good mathematical tool to deal with uncertain information, but it can’t handle unknown data well. The existing approach for data filling in incomplete soft sets can fill data with high accuracy, however it involves a great amount of computation. In this paper a novel approach called simplified approach for data filling in incomplete soft sets (SDFIS) is proposed based on total values of association degrees, which is simpler and easier to understand. The comparison is carried out from several different aspects, and the comparison results show that the proposed approach has low complexity and good usability. The experiments based on UCI data are performed, and the experiment results show that the proposed approach has almost the same accuracy as the existing approach, but it consumes less time.
Bibliographic Details
Elsevier BV
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