Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning

By Hisao Ishibuchi, Yusuke Nojima. International Journal of Approximate Reasoning, 2007

401 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/7e2bb65668288d54e3da9007ed261588241dfab2

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In Artificial Intelligence

Citations in the corpus

A historical review of evolutionary learning methods for Mamdani-type fuzzy rule-based systems: Designing interpretable genetic fuzzy systems (2011, neutral): “In [96], the latter algorithm was generalized as a Pareto-based multiobjective method for interpretability-accuracy tradeoff analysis using NSGA-II.”
Monotonic Calibrated Interpolated Look-Up Tables (2015, background): “Method Monotonicity Strategy Guaranteed Monotonic? Max D Max n Archer and Wang (1993) neural net constrain function yes 2 50 Wang (1994) neural net constrain function yes 1 150 Mukarjee and Stern (1994) kernel estimate post-process yes 2 2447 Ben-David (1995) tree penalize splits yes 8 125 Sill and ”
Explainable Artificial Intelligence: a Systematic Review (2020, methodology): “Both [280, 283] examine the interpretability-accuracy tradeoff in fuzzy rule-based classifiers.”
A peek into the black box: exploring classifiers by randomization (2014, methodology): “Another possible option is to employ methods that explicitly balance accuracy and interpretability (Plate 1999; Ishibuchi and Nojima 2007; Pulkkinen and Koivisto 2008), e.g., allowing the user to specify how losses in predictive performance should be weighed against model complexity.”
Interpretability of linguistic fuzzy rule-based systems: An overview of interpretability measures (2011, methodology): “p AM Ishibuchi and Nojima [38] 2007 CLAS.”
Low-level interpretability and high-level interpretability: a unified view of data-driven interpretable fuzzy system modelling (2008, neutral): “Hence, model interpretability improvement is regarded as one of the most important issues in data-driven fuzzy modelling [5,25,46,83,91,94,93,113,156,174,179,190].”