Looking for a good fuzzy system interpretability index: An experimental approach

Alonso, José M.; Magdalena, Luis; González‐Rodríguez, Gil

doi:10.1016/j.ijar.2009.09.004

Cited by 137 publications

(102 citation statements)

References 43 publications

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“…Interpretability [14,[21][22][23] and accuracy [15] are the two important features considered during the design of fuzzy systems. Basically, interpretability is identified as a feature to understand the significance of something [21], and it is also known with other names like comprehensibility, intelligibility, transparency, readability, understandability, etc.…”

Section: Interpretability Issues In Fkbsmentioning

confidence: 99%

A new approach for tuning interval type-2 fuzzy knowledge bases using genetic algorithms

Shukla

Tripathi

2014

J. Uncertain. Anal. Appl.

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Fuzzy knowledge-based systems (FKBS) are significantly applicable in the area of control, classification, and modeling, having knowledge in the form of fuzzy if-then rules. Type-2 fuzzy theory is used to make these systems more capable of dealing with inherent uncertainties in real-world problems. In this paper, the authors have proposed a genetic tuning approach named lateral displacement and expansion/compression (LDEC) in which α and β parameters are calculated to adjust the parameters of interval type-2 membership functions. α tuning deals with lateral displacement, whereas β tuning carries out compression/expansion operation. The interpretability and accuracy features are considered during the development of this approach. The experimental results show the performance of the proposed approach.

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Section: Interpretability Issues In Fkbsmentioning

confidence: 99%

A new approach for tuning interval type-2 fuzzy knowledge bases using genetic algorithms

Shukla

Tripathi

2014

J. Uncertain. Anal. Appl.

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“…The necessary reduction of rules with weight from the adaptive defuzzification [30][31][32][33]45 , the importance of rule selection [8][9][10][11][12]42,44,48 and the interest in the reduction of rules triggered together 49 , all in order to reduce the system complexity and favour the compactness of the rule base, are the lines followed in our proposal.…”

Section: Description Of the Proposed Mechanismmentioning

confidence: 99%

“…Considering the interpretability and due to its subjective nature and the large amount of factors involved, the choice of appropriate measures is still an open problem 21,28,[30][31][32][33][34] . In the specialist literature, there are proposal of different measures 27,28,[30][31][32][33][34] and techniques [8][9][10]22,23,[35][36][37] for obtaining more interpretable linguistic fuzzy models.…”

Section: Introductionmentioning

confidence: 99%

“…In the specialist literature, there are proposal of different measures 27,28,[30][31][32][33][34] and techniques [8][9][10]22,23,[35][36][37] for obtaining more interpretable linguistic fuzzy models. There are also different proposal for taxonomies [30][31][32][33] to help better understand better the interpretability matter.…”

Section: Introductionmentioning

confidence: 99%

“…They are not the most relevant way to improve the accuracy, but they are well-matched with most other methodologies to do so. However, adaptive defuzzification methods introduce a loss of interpretability 15,[30][31][32][33] , with implications for the overall meaning 15,32 .…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Mechanism to Improve the Interpretability of Linguistic Fuzzy Systems with Adaptive Defuzzification based on the use of a Multi-objective Evolutionary Algorithm

Márquez¹,

Márquez²,

Peregrín³

2012

IJCIS

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This paper proposes a mechanism that helps improve the interpretability of linguistic fuzzy ruled based systems with common adaptive defuzzification methods. Adaptive defuzzification significantly improves the system accuracy, but introduces weights associated with each rule of the rule base, decreasing the system interpretability. The suggested mechanism is based on three goals: 1) reducing the number of total rules considering that rule weight close to zero can be removed; 2) reducing the rules with weights coupled because rules with weights close to one do not need the weight, and 3) reducing rules triggered jointly, all of them by using several metrics and a proposed interpretability index. This is performed using a multi-objective evolutionary algorithm, obtaining a set of solutions with different trade-offs between accuracy and interpretability. In addition, it is important to note that adaptive defuzzification and therefore the proposal developed in this work can be used together with other methodologies to improve system interpretability and accuracy, so it can be viewed as an interesting component.

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Fuzzy association rule mining framework and its application to effective fuzzy associative classification

Kianmehr

Kaya

ElSheikh

et al. 2011

WIREs Data Min & Knowl

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Classification is a technique widely and successfully used for prediction, which is one of the most attractive features of data mining. However, building the classifier is the most challenging part of the process, which proceeds into testing the classifier to check its effectiveness. This article introduces a classification framework that integrates fuzzy association rules into the learning process of machine learning techniques. The integrated framework involves three major components. First, we employ multiobjective optimization twice to decide on the fuzzy sets and then optimize their ranges to extract a set of interesting fuzzy association rules. Second, we use a special subset of the extracted fuzzy association rules, namely, fuzzy class association rules, for building a set of new feature vectors that measure the compatibility between the rules and the given data objects. Third, we train a classifier on the generated feature vectors to predict the class of unseen objects. Most of the earlier algorithms proposed for mining fuzzy association rules assume that the fuzzy sets are given. However, the fuzzy association rule mining component of the proposed framework uses an automated method for autonomous mining of both fuzzy sets and fuzzy association rules. For this purpose, first fuzzy sets are constructed by using a multiobjective genetic algorithm based clustering method for determining and optimizing the membership functions of the fuzzy sets. Then, a method is applied to extract interesting fuzzy association rules. Further, the proposed framework integrates a new layer to the learning process of the machine learning algorithm by constructing the compatibility rule-based feature vectors; this satisfies the aim of better understandability. Once used by the learning algorithm, the compatibility feature vectors represent a rich source of discrimination knowledge that can substantially impact the prediction power of the final classifier. The experimental study and the reported results show the efficiency and effectiveness of our framework for benchmark datasets. In order to further demonstrate and evaluate the applicability of the proposed method to a variety of domains, it is utilized for the task of gene expression classification as well.

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Looking for a good fuzzy system interpretability index: An experimental approach

Cited by 137 publications

References 43 publications

A new approach for tuning interval type-2 fuzzy knowledge bases using genetic algorithms

A new approach for tuning interval type-2 fuzzy knowledge bases using genetic algorithms

A Mechanism to Improve the Interpretability of Linguistic Fuzzy Systems with Adaptive Defuzzification based on the use of a Multi-objective Evolutionary Algorithm

Fuzzy association rule mining framework and its application to effective fuzzy associative classification

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