Multi-objective design of highly interpretable fuzzy rule-based classifiers with semantic cointension

Cannone, R.; Alonso, José M.; Magdalena, Luis

doi:10.1109/gefs.2011.5949502

Cited by 15 publications

(3 citation statements)

References 32 publications

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“…The comprehension of such inference mechanism is not straightforward [36]. However, it is a key issue to properly interpret the behavior of the FRBSs built from data.…”

Section: Analyzing Furia Inference Mechanism By Fingramsmentioning

confidence: 99%

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Enhancing Fingrams to deal with precise fuzzy systems

Pancho

Alonso

Magdalena

2016

Fuzzy Sets and Systems

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“…The comprehension of such inference mechanism is not straightforward [36]. However, it is a key issue to properly interpret the behavior of the FRBSs built from data.…”

Section: Analyzing Furia Inference Mechanism By Fingramsmentioning

confidence: 99%

“…However, although FURIA produces compact rule bases, its interpretability is arguable [36], being penalized by the absence of linguistic readability. Though FURIA usually generates low number of rules (and antecedents per rule), they lack of linguistic readability because there is no global semantics.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Fingrams to deal with precise fuzzy systems

Pancho

Alonso

Magdalena

2016

Fuzzy Sets and Systems

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“…Explicit semantics (fuzzy sets, operators, inference engine) and implicit semantics (knowledge gathered by user) are compared using a co-intension approach called Semantic Co-intension. A novel index has been proposed in [110] for designing highly interpretable rule-based classifiers, based on Semantic Co-intension.…”

Section: Semantic Co-intension Approachmentioning

confidence: 99%

A Review on the Interpretability-Accuracy Trade-Off in Evolutionary Multi-Objective Fuzzy Systems (EMOFS)

Shukla

Tripathi

2012

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Interpretability and accuracy are two important features of fuzzy systems which are conflicting in their nature. One can be improved at the cost of the other and this situation is identified as “Interpretability-Accuracy Trade-Off”. To deal with this trade-off Multi-Objective Evolutionary Algorithms (MOEA) are frequently applied in the design of fuzzy systems. Several novel MOEA have been proposed and invented for this purpose, more specifically, Non-Dominated Sorting Genetic Algorithms (NSGA-II), Strength Pareto Evolutionary Algorithm 2 (SPEA2), Fuzzy Genetics-Based Machine Learning (FGBML), (2 + 2) Pareto Archived Evolutionary Strategy ((2 + 2) PAES), (2 + 2) Memetic- Pareto Archived Evolutionary Strategy ((2 + 2) M-PAES), <em>etc</em>. This paper introduces and reviews the approaches to the issue of developing fuzzy systems using Evolutionary Multi-Objective Optimization (EMO) algorithms considering ‘Interpretability-Accuracy Trade-off’ and mainly focusing on the work in the last decade. Different research issues and challenges are also discussed

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