What is it about?
In the present communication, we give preliminaries and definitions in Sect. 2 and an algorithm to estimate missing values in fuzzy matrix is described and illustrated with a numerical example in Sect. 3. In Sect. 4, an algorithm to estimate the missing values in interval-valued fuzzy matrix is explained and applied in a numerical problem. Discussion and comparison are given in Sect. 5 with the conclusion in Sect. 6.
Featured Image
Why is it important?
In our real-life problems, uncertainty also occurs due to loss of information and vagueness which causes information loss. Thus, information loss ultimately results in incomplete information. There are many other reasons which are also responsible for incompleteness, for example, erroneous data measure, insufficient data collection, lack of evidence, etc.
Perspectives
It is hoped that this technique would be very helpful in estimating missing values in management, economics, and medical sciences. This technique will be useful in finding missing or illegible values of field experiments data which can be repeated due to climate conditions. In the same way, the missing data of a patient can cause difficulty in diagnose of disease and that can be overcome by applying the technique represented in this paper. The technique can also be applied in estimating the missing values in intuitionist fuzzy matrix.
Prof. D S Hooda
Guru Jambheshwar University of Science and Technology
Read the Original
This page is a summary of: Estimation of missing values in fuzzy matrices (FM) and interval-valued fuzzy matrices (IVFM), Life Cycle Reliability and Safety Engineering, March 2020, Springer Science + Business Media,
DOI: 10.1007/s41872-020-00116-1.
You can read the full text:
Contributors
The following have contributed to this page
