Interpretability issues in fuzzy modeling

By Jorge Casillas. 2003

459 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Financial volatility trading using a self-organising neural-fuzzy semantic network and option straddle-based approach (2010, background): “(3) is also known as linguistic fuzzy modeling (LFM) (Casillas, Cordn, Herrera, & Magdalena, 2003), and best approximates the semantic knowledge (e.g., long-established knowledge about objects, facts, and word meanings etc.) representation embodied in the human mind (Toth, 1997).”
FLEXFIS: A Robust Incremental Learning Approach for Evolving Takagi–Sugeno Fuzzy Models (2008, background): “, they are applied in areas such as identification [2] and system analysis (as they result in accurate models as well as linguistic interpretable models [3] in the form of rule bases, and therefore, may yield a better understanding of some underlying system behaviors than pure black box models), con”
Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning (2007, background): “Thus the existence of the accuracy-complexity tradeoff in the design of fuzzy rule-based systems has been realized [5] [6].”
Stock Trading Using RSPOP: A Novel Rough Set-Based Neuro-Fuzzy Approach (2006, methodology): “The fuzzy modeling research field is divided into two areas: linguistic fuzzy modeling that is focused on interpretability, mainly the Mamdani model [37] given in (2) and (3) (please refer to [39] for a recent comprehensive coverage on interpretability issues of fuzzy modeling) [31], [39], [40].”
Filter Bank Common Spatial Pattern (FBCSP) in Brain-Computer Interface (2008, background): “Neuro-Fuzzy Systems can be classified into linguistic or precise model [30].”
A historical review of evolutionary learning methods for Mamdani-type fuzzy rule-based systems: Designing interpretable genetic fuzzy systems (2011, unknown): “A new tendency in the fuzzy modeling scientific community that looks for a good balance between interpretability and accuracy has thus increased in importance in the last few years [1] [34] [37] [81] [118] [148] [155].”