A multi-objective evolutionary algorithm with an interpretability improvement mechanism for linguistic fuzzy systems with adaptive defuzzification

Márquez, Antonio A.; Márquez, Francisco Alfredo; Peregrín, Antonio

doi:10.1109/fuzzy.2010.5584294

Cited by 28 publications

(13 citation statements)

References 19 publications

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“…On the other hand, the accuracy obtained by (22) is 96.1%, and similar results are gained if consequents are in the form (14), while accuracy decreases to 92.7% if rule weights are avoided. Therefore, the use of rule weights demonstrate a great power in improving per-formance, for both level I and II output, while fuzzy consequents enable to refine the output of level II.…”

Section: Application To Real Datasupporting

confidence: 57%

“…In particular, MISO systems with conjunctive implication are considered. Instead, the average number of firing rules [1][2][3] will be considered to assess the understandability of the inference process, and the opportunity of using rule weights [4,5,7,14] will be evaluated, since they reduce the system interpretability [7,14] but can greatly improve system performances [4,5].…”

Section: Interpretability Issuesmentioning

confidence: 99%

“…From the interpretability perspective, two opposite effects are obtained: on the one hand, each rule with probabilities (19) in the consequent (level II/III consequent) is much more interpretable than the same rule in the form (18) (level V consequent); on the other hand, the whole rule base is much less interpretable [7,14] if weights (20) are applied.…”

Section: Rule Consequents Can Be Of Level I (Eg "Then Y Ismentioning

confidence: 99%

See 2 more Smart Citations

Insights into Interpretability of Neuro-Fuzzy Systems

Pota¹,

Esposito²

2015

Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and

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Neuro-fuzzy networks revealed their proficiency in learning from data, while offering a transparent and somehow interpretable rule-based model. Recent research focused either on the interpretability of the chosen model or on the system performance. Regarding the interpretability, here an index to control the trade-off between complexity and performance, some insights into fuzzy partitions properties, an ideal fuzzy sets shape, and an evaluation of rules are proposed. All the evaluations are made taking into account the required output and performance. A discussion on results of a system built using the Wisconsin Breast Cancer Dataset is performed as a proof of concept.

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Section: Application To Real Datasupporting

confidence: 57%

Section: Interpretability Issuesmentioning

confidence: 99%

Section: Rule Consequents Can Be Of Level I (Eg "Then Y Ismentioning

confidence: 99%

See 1 more Smart Citation

Insights into Interpretability of Neuro-Fuzzy Systems

Pota¹,

Esposito²

2015

Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and

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“…In a previous work 51 , we used the minimization of the #R F and R W _AvR TG as an objective, but in the current study we only use the index R W _AvR TG . This is due to the fact that #R F will be also minimized when minimizing the interpretability index, because it includes the R W and uses the T L mechanism to reduce the number of rules (section 3.1.1), so when minimizing the R W through the interpretability index, the #R F will be also minimized.…”

Section: Objectivesmentioning

confidence: 99%

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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“…[3,18,31,99]) and using multi-objective methods (see e.g. [57,85,86,100]). (c) Use of population-based algorithms to obtain interpretable systems (see e.g.…”

Section: Introductionmentioning

confidence: 99%

A New Interpretability Criteria for Neuro-Fuzzy Systems for Nonlinear Classification

Łapa

Cpałka

Galushkin

2015

Artificial Intelligence and Soft Computing

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Abstract. In this paper a new approach for construction of neuro-fuzzy systems for nonlinear classification is introduced. In particular, we concentrate on the flexible neuro-fuzzy systems which allow us to extend notation of rules with weights of fuzzy sets. The proposed approach uses possibilities of hybrid evolutionary algorithm and interpretability criteria of expert knowledge. These criteria include not only complexity of the system, but also semantics of the rules. The approach presented in our paper was tested on typical nonlinear classification simulation problems.

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A multi-objective evolutionary algorithm with an interpretability improvement mechanism for linguistic fuzzy systems with adaptive defuzzification

Cited by 28 publications

References 19 publications

Insights into Interpretability of Neuro-Fuzzy Systems

Insights into Interpretability of Neuro-Fuzzy Systems

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

A New Interpretability Criteria for Neuro-Fuzzy Systems for Nonlinear Classification

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