Interpretability Issues in Evolutionary Multi-Objective Fuzzy Knowledge Base Systems

Shukla, Praveen Kumar; Tripathi, Surya Prakash

doi:10.1007/978-81-322-1038-2_40

Cited by 13 publications

(8 citation statements)

References 57 publications

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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%

Identifying Effectiveness of Supply Chain Management Using Fuzzy Customer Feedback System

Ahmad

Purohit

Darbari

2017

IJMUE

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Section: Related Workmentioning

confidence: 99%

Identifying Effectiveness of Supply Chain Management Using Fuzzy Customer Feedback System

Ahmad

Purohit

Darbari

2017

IJMUE

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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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“…Therefore, during the last few years many researchers focused on obtaining a compromise between accuracy and interpretability of fuzzy systems (see, e.g., Zhou and Gan, 2008;Casillas et al, 2003;Di Nuovo and Ascia, 2013;Ishibashi and Lucio Nascimento, Jr., 2013;Shukla and Tripathi, 2013;Juang and Chen, 2013;Lughofer, 2013;Johansen et al, 2000).…”

Section: 2mentioning

confidence: 99%

“…Interpretability of fuzzy models can be provided in many ways, but restrictions on the learning process are imposed most commonly (see, e.g., Lughofer, 2013;Cpałka et al, 2014;Shukla and Tripathi, 2013;Ishibashi and Lucio Nascimento, Jr., 2013).…”

Section: 2mentioning

confidence: 99%

A new approach to nonlinear modelling of dynamic systems based on fuzzy rules

Bartczuk

Przybył

Cpałka

2016

International Journal of Applied Mathematics and Computer Science

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For many practical weakly nonlinear systems we have their approximated linear model. Its parameters are known or can be determined by one of typical identification procedures. The model obtained using these methods well describes the main features of the system's dynamics. However, usually it has a low accuracy, which can be a result of the omission of many secondary phenomena in its description. In this paper we propose a new approach to the modelling of weakly nonlinear dynamic systems. In this approach we assume that the model of the weakly nonlinear system is composed of two parts: a linear term and a separate nonlinear correction term. The elements of the correction term are described by fuzzy rules which are designed in such a way as to minimize the inaccuracy resulting from the use of an approximate linear model. This gives us very rich possibilities for exploring and interpreting the operation of the modelled system. An important advantage of the proposed approach is a set of new interpretability criteria of the knowledge represented by fuzzy rules. Taking them into account in the process of automatic model selection allows us to reach a compromise between the accuracy of modelling and the readability of fuzzy rules.

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Interpretability Issues in Evolutionary Multi-Objective Fuzzy Knowledge Base Systems

Cited by 13 publications

References 57 publications

Identifying Effectiveness of Supply Chain Management Using Fuzzy Customer Feedback System

Identifying Effectiveness of Supply Chain Management Using Fuzzy Customer Feedback System

Social Perception Grading of Hospitals using Fuzzy Knowledge Base Systems

A new approach to nonlinear modelling of dynamic systems based on fuzzy rules

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