Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Fatemeh Aghaeipoor"'
Publikováno v:
IEEE Transactions on Fuzzy Systems. :1-12
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c692a82b37667f1095333d729d905b21
https://doi.org/10.1109/tfuzz.2023.3243935
https://doi.org/10.1109/tfuzz.2023.3243935
Publikováno v:
IEEE Transactions on Fuzzy Systems. 30:830-840
In current Data Science applications, the course of action has derived to adapt the system behavior for the human cognition, resulting in the emerging area of explainable artificial intelligence. Among different classification paradigms, those based
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::89b884ecac98b29d4af99a73000b73b5
https://doi.org/10.1109/tfuzz.2021.3049911
https://doi.org/10.1109/tfuzz.2021.3049911
Publikováno v:
Information Sciences. 496:1-24
This work presents a multi-objective evolutionary linguistic fuzzy system that addresses regression problems, especially those that are dimensional and scalable. A multi-objective knowledge base learning (MOKBL) is developed in the first stage of thi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c83636d066d6934edf2110c4a166ef37
https://doi.org/10.1016/j.ins.2019.04.035
https://doi.org/10.1016/j.ins.2019.04.035
Publikováno v:
Applied Soft Computing. 79:283-299
Fuzzy Rule-Based Systems, FRBSs, are powerful tools to address regression problems. They can model the relationship between inputs and outputs by linguistic concepts. However, those FRBSs which are based on the conventional Type-1 fuzzy sets may not
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::326cb32d7439856061d75403b8b1c278
https://doi.org/10.1016/j.asoc.2019.03.047
https://doi.org/10.1016/j.asoc.2019.03.047
Autor:
Fatemeh Aghaeipoor, Mahdi Eftekhari
Publikováno v:
Soft Computing. 23:11737-11757
Fuzzy rule-based systems, due to their simplicity and comprehensibility, are widely used to solve regression problems. Fuzzy rules can be generated by learning from data examples. However, this strategy may result in high numbers of rules that most o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7e3b6493d89f50a6957232687c22cb3f
https://doi.org/10.1007/s00500-018-03726-1
https://doi.org/10.1007/s00500-018-03726-1
Publikováno v:
FUZZ-IEEE
Big data classification problems are known to be no longer addressable by sequential algorithms. Therefore, it is necessary to design and develop novel solutions to provide accurate yet interpretable models in a tolerable elapsed time. In this area,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::459976f14093d8344cf45de907a46705
https://doi.org/10.1109/fuzz48607.2020.9177626
https://doi.org/10.1109/fuzz48607.2020.9177626
Publikováno v:
Expert Systems with Applications. 162:113859
One of the most important factors affecting the interpretability of Fuzzy Rule-Based Systems (FRBSs) is the number of features used Indeed, the employment of a large number of features could be problematic for the components of an FRBS. In these case
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a7a8b22314d818408e984c556041fa0d
https://doi.org/10.1016/j.eswa.2020.113859
https://doi.org/10.1016/j.eswa.2020.113859