Data driven fuzzy membership function generation for increased understandability

Wijayasekara, Dumidu; Manic, Milos

doi:10.1109/fuzz-ieee.2014.6891547

Cited by 14 publications

(10 citation statements)

References 24 publications

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“…They can also be represented by membership functions and their interpretability will be subjected to specific concepts. For most applications, there is no optimal way to generate MFs so they must relay on data characteristics and a fixed conceptual framework (Wijayasekara & Manic, 2014). Membership itself can be so important that some proposals which base their MFs on typicality (distance to a prototype) faces the problem of choosing the adequate distance measure like by Setnes (2000).…”

Section: Uncertainty Basismentioning

confidence: 99%

“…The understandability compliance proposed by Wijayasekara and Manic (2014) shows a very contrasting approach based on human interpretation of a fuzzy system. Similarly, Alikhademi and Zainudin (2014) tried to find fuzzy sets which are interpretable rather than precise as ''the main role of fuzzy sets and MFs is transforming quantitative values to linguistic terms''.…”

Section: Introductionmentioning

confidence: 99%

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A new approach to uncertainty description through accomplishment membership functions

Ibarra

Ponce

Molina

2015

Expert Systems with Applications

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Section: Uncertainty Basismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A new approach to uncertainty description through accomplishment membership functions

Ibarra

Ponce

Molina

2015

Expert Systems with Applications

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“…Similar approach is applied to both the traffic congestion and VOTTS variables ( Figures A1 and A2 Appendix A). There are various methods of constructing membership functions for linguistic variables [41,42] but this approach is adopted for simplicity. In practical applications, it is expected that membership functions and linguistic variables will be selected based on expert opinion with contribution from both the government and citizens or by optimization [35][36][37][38].…”

Section: Linguistic Variablesmentioning

confidence: 99%

Towards a Fair and More Transparent Rule-Based Valuation of Travel Time Savings

Adjenughwure

Papadopoulos

2019

Sustainability

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The value of travel time savings (VOTTS) is one of the most important variables for calculating the benefits of transportation projects. However, the way it is currently calculated (usually via discrete choice models) is complex, tedious and subject to a reasonable level of uncertainty. Furthermore, the method is not easily understood by government officials who use the VOTTS for appraisal and the citizens are not fully aware how such values are calculated. This lack of understanding and transparency in methodology may lead to misuse of the VOTTS during transport project appraisals which in turn can result in unfair transport decisions for citizens, government and the environment. To solve these problems, a fuzzy logic rule-based approach is proposed. With this approach, the rules can be made based on economic and behavioral theories by experts, government officials and citizens (via surveys). This approach makes it understandable to everyone how values are calculated. To test the applicability of the approach, a simple numerical example is presented by estimating the VOTTS of various countries using their gross domestic product-purchasing power parity (GDP-PPP) and the traffic congestion level. Results are then compared to values obtained from a recent metanalysis on VOTTS in Europe and some official VOTTS.

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“…Recently, the concept of strong fuzzy partition was used to construct the set MF [10,16]. The concept is defined as follows: the set of MFs makes a strong fuzzy partition if they cover the domain of the attribute value and at any point on the specified domain, the total of fuzzy degrees of this point to all MFs in the partition gain the value of 1.…”

Section: The Problem Of Dividing a Determined Fuzzy Domainmentioning

confidence: 99%

Partition Fuzzy Domain With Multi-Gnanularity Representation of Data Basing on Hedge Algebra Approach

Anh¹,

Son²

2018

JCC

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This paper presents methods of dividing quantitative attributes into fuzzy domains with multi-granularity representation of data based on hedge algebra approach. According to this approach, more information is expressed from general to specific knowledge by explored association rules. As a result, this method brings a better response than the one using usual single-granularity representation of data. Furthermore, it meets the demand of the authors as the number of exploring rules is higher.

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Data driven fuzzy membership function generation for increased understandability

Cited by 14 publications

References 24 publications

A new approach to uncertainty description through accomplishment membership functions

A new approach to uncertainty description through accomplishment membership functions

Towards a Fair and More Transparent Rule-Based Valuation of Travel Time Savings

Partition Fuzzy Domain With Multi-Gnanularity Representation of Data Basing on Hedge Algebra Approach

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