Interpretability of linguistic fuzzy rule-based systems: An overview of interpretability measures

Gacto, María José; Alcalá, Rafael; Herrera, F.

doi:10.1016/j.ins.2011.02.021

Cited by 486 publications

(258 citation statements)

References 74 publications

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“…There exist many measures of interpretability. To evaluate the interpretability, [26] proposed two categories of measures: complexity-based interpretability and semantic-based interpretability.…”

Section: Interpretability Of the Modelmentioning

confidence: 99%

“…Usually, classic means to evaluate this interpretability are based on its complexity: the number of (fuzzy) rules the model is composed of, the number of variables in each rule, the use of meaningful fuzzy sets [10,26]. However, the interpretability of the structure of the model that should be understood clearly by the end user can lead to the choice of a specific algorithm.…”

Section: Interpretability Of the Modelmentioning

confidence: 99%

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Fuzzy data mining and management of interpretable and subjective information

Marsala

Bouchon‐Meunier

2015

Fuzzy Sets and Systems

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Fuzzy set theory offers an important contribution to data mining leading to fuzzy data mining. It enables the management of interpretable and subjective information in both input and output of the data mining process. In this paper, we discuss the notion of interpretability in fuzzy data mining and we present some references on the management of emotions as a particular kind of subjective information.

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Section: Interpretability Of the Modelmentioning

confidence: 99%

Section: Interpretability Of the Modelmentioning

confidence: 99%

Fuzzy data mining and management of interpretable and subjective information

Marsala

Bouchon‐Meunier

2015

Fuzzy Sets and Systems

View full text Add to dashboard Cite

show abstract

“…A deeper discussion and review of various complexity metrics can be found in Ref. 50. Considering the sth classifier, CI is calculated through: with X ′ representing the normalized value of the X measure, obtained through:…”

Section: Comparative Analysismentioning

confidence: 99%

Reducing the Complexity of Genetic Fuzzy Classifiers in Highly-Dimensional Classification Problems

Stavrakoudis¹,

Galidaki²,

Gitas³

et al. 2012

IJCIS

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This paper introduces the Fast Iterative Rule-based Linguistic Classifier (FaIRLiC), a Genetic Fuzzy Rule-Based Classification System (GFRBCS) which targets at reducing the structural complexity of the resulting rule base, as well as its learning algorithm's computational requirements, especially when dealing with high-dimensional feature spaces. The proposed methodology follows the principles of the iterative rule learning (IRL) approach, whereby a rule extraction algorithm (REA) is invoked in an iterative fashion, producing one fuzzy rule at a time. The REA is performed in two successive steps: the first one selects the relevant features of the currently extracted rule, whereas the second one decides the antecedent part of the fuzzy rule, using the previously selected subset of features. The performance of the classifier is finally optimized through a genetic tuning post-processing stage. Comparative results in a hyperspectral remote sensing classification as well as in 12 real-world classification datasets indicate the effectiveness of the proposed methodology in generating high-performing and compact fuzzy rule-based classifiers, even for very high-dimensional feature spaces.

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“…It has been extensively studied in the case of fuzzy rule-based systems [1,2,3,4], which have been the most widespread within all fuzzy modelling methods, mainly because of the early success of fuzzy expert systems and fuzzy control, both based on rules of the form "if V 1 is A 1 and V 2 is A 2 , ..., then W is B". These studies point out the difficulty to assess interpretability and provide various kinds of properties involved in its evaluation.…”

Section: Introductionmentioning

confidence: 99%

Interpretability of fuzzy linguistic summaries

Lesot

Moyse

Bouchon‐Meunier

2016

Fuzzy Sets and Systems

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This paper investigates the question of the interpretability of fuzzy linguistic summaries, both at the sentence level and at the summary level, seen as a set of sentences. The individual sentence interpretability is examined as depending both on its representativity measured by a quality degree and on its linguistic expression. Different properties at the summary level are also discussed, namely their consistency, their non redundancy and the information they convey.

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Interpretability of linguistic fuzzy rule-based systems: An overview of interpretability measures

Cited by 486 publications

References 74 publications

Fuzzy data mining and management of interpretable and subjective information

Fuzzy data mining and management of interpretable and subjective information

Reducing the Complexity of Genetic Fuzzy Classifiers in Highly-Dimensional Classification Problems

Interpretability of fuzzy linguistic summaries

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