Table 1 Interpretability in type-1 FKBS
From: A new approach for tuning interval type-2 fuzzy knowledge bases using genetic algorithms
Year | Authors | Description of work | Reference |
|---|---|---|---|
2000 | Y. Jin | Interpretability improvement in high-dimensional fuzzy systems | [23] |
2001 | S. Guillaume | Automatic rule generation and structure optimization for maintaining interpretability | [27] |
2005 | R. Mikut et al. | Maintaining interpretability in data-based fuzzy system development along with user-controllable trade-off in between interpretability and accuracy | [28] |
2006 | R. Alcala et al. | Seven hybrid techniques for developing accurate and interpretable FKBS | [16] |
2008, 2012 | J. M. Alonso et al. | Highly interpretable linguistic knowledge (HILK) utilizing the features of LFM and PFM | |
2008 | S. M. Zhou and J. A. Gan | Identification of two interpretability levels: low level on the fuzzy set and high level on the fuzzy rule | [31] |
2008 | C. Mencar and A. M. Fanelli | Introduction of semantic constraints, distinguishability, coverage, convexity, and normality | [32] |
2009 | J. M. Alonso et al. | Conceptual framework for assessing the interpretability based on two issues: ‘description’ and ‘explanation’ | [22] |
2011 | M. J. Gacto et al. | A proposal of double-axis taxonomy: ‘complexity and semantic interpretability’ and ‘rule base and fuzzy partition’ | [33] |
2013 | M. Fazzolari et al. | I-A Trade-Off handling with instance selection techniques | [34] |