A Survey on Interpretability-Accuracy (I-A) Trade-Off in Evolutionary Fuzzy Systems

Shukla, Praveen Kumar; Tripathi, Surya Prakash

doi:10.1109/icgec.2011.32

Cited by 22 publications

(11 citation statements)

References 42 publications

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“…Evolutionary multi-objective optimization is one of the strategies to deal with interpretabilityaccuracy trade-off fuzzy knowledge base system or fuzzy classifiers [9][10][11][12].…”

Section: Related Workmentioning

confidence: 99%

Social Perception Grading of Hospitals using Fuzzy Knowledge Base Systems

Karn

Darbari

Shukla³

2017

Fifth International Conference on Advances in Computing, Communication and Information Technology - CCIT 2017

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Abstract.Proper health is an important parameter to ensure the socioeconomic development of the country. Hospitals are playing vital role to improve the health standards in the life and serves the society very effectively. The assessment of quality and ease of medical facilities provided by the hospitals is an important research line. The higher quality of medical care improves the patient satisfaction leading to social perception enhancement. In this paper, we are investigating a new Expert System to assess the quality of medical care of any hospital depending on few parameters. The system is developed using fuzzy knowledge based systems and implemented in Guaje. The system would be generating the grades of different hospitals as per the quality care provided by them.

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“…Evolutionary multi-objective optimization is one of the strategies to deal with interpretabilityaccuracy trade-off fuzzy knowledge base system or fuzzy classifiers [9][10][11][12].…”

Section: Related Workmentioning

confidence: 99%

Social Perception Grading of Hospitals using Fuzzy Knowledge Base Systems

Karn

Darbari

Shukla³

2017

Fifth International Conference on Advances in Computing, Communication and Information Technology - CCIT 2017

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“…Computing similarity allows merging similar fuzzy sets so as to get simplified fuzzy model with accepted accuracy and appropriate size of fuzzy rules that can be linguistically described [1,2,3,4]. Most of the approaches found in literature for computing similarity measures of convex and continuously-shaped fuzzy sets are mainly numeric [5,6], or using approximate mathematical formula for the considered fuzzy sets [7,8,9].…”

Section: Introductionmentioning

confidence: 99%

Analytical formulas for similarity, possibility and distinguishability measures of Cauchy type fuzzy sets with comparison to Gaussian fuzzy sets

Amer

Hefny

2015

2015 IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS)

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Abstrac� This paper provides general analytical formulas for similarity and distinguishabilty measures of fuzzy sets of Cauchy type membership functions. A generalized analytical formula between similarity and possibility measures has also been obtained. A comparison with the case of Gaussian fuzzy sets ensures interesting monotonic characteristic charts for Cauchy type fuzzy sets compared with those of Gaussian fuzzy sets. This result represents a significant guide for building interpretable fuzzy models by adopting suitable forms of fuzzy sets as linguistic values based on their characteristic charts.

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“…An improvement in interpretability-accuracy trade-off is well addressed in [13,[17][18][19]. A new optimization based interval type-2 fuzzy knowledge base system has been developed with an improvement strategy of LDEC approach in [14].…”

Section: Related Workmentioning

confidence: 99%