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ORCIDFatemeh Aghaeipoor
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2020 – today
- 2023
[j6]Fatemeh Aghaeipoor
, Mohammad Sabokrou, Alberto Fernández:
Fuzzy Rule-Based Explainer Systems for Deep Neural Networks: From Local Explainability to Global Understanding. IEEE Trans. Fuzzy Syst. 31(9): 3069-3080 (2023)- [i2]Fatemeh Aghaeipoor, Dorsa Asgarian, Mohammad Sabokrou:
Expanding Explainability Horizons: A Unified Concept-Based System for Local, Global, and Misclassification Explanations. CoRR abs/2306.03531 (2023) - [i1]Rojina Kashefi, Leili Barekatain, Mohammad Sabokrou, Fatemeh Aghaeipoor:
Explainability of Vision Transformers: A Comprehensive Review and New Perspectives. CoRR abs/2311.06786 (2023) - 2022
- [j5]Fatemeh Aghaeipoor, Mohammad Masoud Javidi, Alberto Fernández:
IFC-BD: An Interpretable Fuzzy Classifier for Boosting Explainable Artificial Intelligence in Big Data. IEEE Trans. Fuzzy Syst. 30(3): 830-840 (2022) - 2020
- [j4]Fatemeh Aghaeipoor, Mohammad Masoud Javidi:
A hybrid fuzzy feature selection algorithm for high-dimensional regression problems: An mRMR-based framework. Expert Syst. Appl. 162: 113859 (2020) - [c1]Fatemeh Aghaeipoor, Mohammad Masoud Javidi, Isaac Triguero, Alberto Fernández:
Chi-BD-DRF: Design of Scalable Fuzzy Classifiers for Big Data via A Dynamic Rule Filtering Approach. FUZZ-IEEE 2020: 1-7
2010 – 2019
- 2019
- [j3]Fatemeh Aghaeipoor, Mohammad Masoud Javidi:
On the influence of using fuzzy extensions in linguistic fuzzy rule-based regression systems. Appl. Soft Comput. 79: 283-299 (2019) - [j2]Fatemeh Aghaeipoor, Mohammad Masoud Javidi:
MOKBL+MOMs: An interpretable multi-objective evolutionary fuzzy system for learning high-dimensional regression data. Inf. Sci. 496: 1-24 (2019) - [j1]Fatemeh Aghaeipoor, Mahdi Eftekhari:
EEFR-R: extracting effective fuzzy rules for regression problems, through the cooperation of association rule mining concepts and evolutionary algorithms. Soft Comput. 23(22): 11737-11757 (2019)
Coauthor Index
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