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

Shukla, Praveen Kumar; Tripathi, Surya Prakash

doi:10.3390/info3030256

Cited by 31 publications

(15 citation statements)

References 111 publications

(101 reference statements)

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“…Accuracy and interpretability features are contradictory with each other; one can be improved at the cost of the other. This is called the interpretability-accuracy trade-off [6,8,14]. Few of the knowledge base systems are also developed in advanced fuzzy methods, like interval type-2 fuzzy sets [7,13].…”

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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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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“…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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“…Encoding: Before starting to solve the problem with GA, the appropriate encoding technique must be applied to represent the individuals that are related to the problem domain in a form of genes with specific length. The type of problem determines the encoding technique used [37][38][39]. Below, some encoding techniques are introduced:…”

Section: Basic Principles Of Genetic Algorithmsmentioning

confidence: 99%

An Improved Genetic Algorithm with a New Initialization Mechanism Based on Regression Techniques

et al. 2018

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Genetic algorithm (GA) is one of the well-known techniques from the area of evolutionary computation that plays a significant role in obtaining meaningful solutions to complex problems with large search space. GAs involve three fundamental operations after creating an initial population, namely selection, crossover, and mutation. The first task in GAs is to create an appropriate initial population. Traditionally GAs with randomly selected population is widely used as it is simple and efficient; however, the generated population may contain poor fitness. Low quality or poor fitness of individuals may lead to take long time to converge to an optimal (or near-optimal) solution. Therefore, the fitness or quality of initial population of individuals plays a significant role in determining an optimal or near-optimal solution. In this work, we propose a new method for the initial population seeding based on linear regression analysis of the problem tackled by the GA; in this paper, the traveling salesman problem (TSP). The proposed Regression-based technique divides a given large scale TSP problem into smaller sub-problems. This is done using the regression line and its perpendicular line, which allow for clustering the cities into four sub-problems repeatedly, the location of each city determines which category/cluster the city belongs to, the algorithm works repeatedly until the size of the subproblem becomes very small, four cities or less for instance, these cities are more likely neighboring each other, so connecting them to each other creates a somehow good solution to start with, this solution is mutated several times to form the initial population. We analyze the performance of the GA when using traditional population seeding techniques, such as the random and nearest neighbors, along with the proposed regression-based technique. The experiments are carried out using some of the well-known TSP instances obtained from the TSPLIB, which is the standard library for TSP problems. Quantitative analysis is carried out using the statistical test tools: analysis of variance (ANOVA), Duncan multiple range test (DMRT), and least significant difference (LSD). The experimental results show that the performance of the GA that uses the proposed regression-based technique for population seeding outperforms other GAs that uses traditional population seeding techniques such as the random and the nearest neighbor based techniques in terms of error rate, and average convergence.

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

Cited by 31 publications

References 111 publications

Social Perception Grading of Hospitals using Fuzzy Knowledge Base Systems

Social Perception Grading of Hospitals using Fuzzy Knowledge Base Systems

Identifying Effectiveness of Supply Chain Management Using Fuzzy Customer Feedback System

An Improved Genetic Algorithm with a New Initialization Mechanism Based on Regression Techniques

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