TY  - JOUR
AU  - Bmotra, Mansi
AU  - Kumar, Rakesh
AU  - Dhiman, Gaurav
PY  - 2025
DA  - 2025/11/08
TI  - Comparative Study of Pentagonal and Hexagonal Fuzzy Membership Function Using Credibility Theory in Machine Learning Systems
JO  - ICCK Transactions on Machine Intelligence
T2  - ICCK Transactions on Machine Intelligence
JF  - ICCK Transactions on Machine Intelligence
VL  - 1
IS  - 3
SP  - 127
EP  - 137
DO  - 10.62762/TMI.2025.922612
UR  - https://www.icck.org/article/abs/TMI.2025.922612
KW  - fuzzy sets
KW  - credibility theory
KW  - membership functions
KW  - machine learning system
AB  - The paper carries out a comparative study that is based on the use of credibility theory to examine pentagonal and hexagonal fuzzy membership functions of machine learning systems. These fuzzy memberships can be used to manage the uncertainty and imprecision of a data driven-model which allows better decision-making in the case of vague or incomplete information. The credibility theory is used to determine quantitatively the reliability of the inferences obtained through each function. Both the membership functions are modelled, incorporated in machine learning framework and tested on randomly generated as well as application specific datasets. The results obtained indicate that the performance of the hexagonal function is better than that of the pentagonal one in most cases except in Case 4, the latter performs better. The results provide excellent recommendations in implementing fuzzy logic that can be applied to engineering, finance, and decision-support systems. Future directions could include more complicated shapes of the functions used, larger data sets and the ability of the results to be scaled up in the real world.
SN  - 3068-7403
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Bmotra2025Comparativ,
  author = {Mansi Bmotra and Rakesh Kumar and Gaurav Dhiman},
  title = {Comparative Study of Pentagonal and Hexagonal Fuzzy Membership Function Using Credibility Theory in Machine Learning Systems},
  journal = {ICCK Transactions on Machine Intelligence},
  year = {2025},
  volume = {1},
  number = {3},
  pages = {127-137},
  doi = {10.62762/TMI.2025.922612},
  url = {https://www.icck.org/article/abs/TMI.2025.922612},
  abstract = {The paper carries out a comparative study that is based on the use of credibility theory to examine pentagonal and hexagonal fuzzy membership functions of machine learning systems. These fuzzy memberships can be used to manage the uncertainty and imprecision of a data driven-model which allows better decision-making in the case of vague or incomplete information. The credibility theory is used to determine quantitatively the reliability of the inferences obtained through each function. Both the membership functions are modelled, incorporated in machine learning framework and tested on randomly generated as well as application specific datasets. The results obtained indicate that the performance of the hexagonal function is better than that of the pentagonal one in most cases except in Case 4, the latter performs better. The results provide excellent recommendations in implementing fuzzy logic that can be applied to engineering, finance, and decision-support systems. Future directions could include more complicated shapes of the functions used, larger data sets and the ability of the results to be scaled up in the real world.},
  keywords = {fuzzy sets, credibility theory, membership functions, machine learning system},
  issn = {3068-7403},
  publisher = {Institute of Central Computation and Knowledge}
}