HILK: A new methodology for designing highly interpretable linguistic knowledge bases using the fuzzy logic formalism

Alonso, José M.; Magdalena, Luis; Guillaume, Serge

doi:10.1002/int.20288

Cited by 88 publications

(52 citation statements)

References 41 publications

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“…A new tendency in the fuzzy modeling scientific community that looks for a good balance between interpretability and accuracy has thus increased in importance in the last few years [1,12,21,23,53,71]. The term fuzzy modeling interpretability-accuracy tradeoff [21,23] has been coined to define it.…”

Section: O Cordónmentioning

confidence: 98%

“…In the last few years, it has been shown an increasing interest on considering fuzzy techniques to design both accurate and interpretable fuzzy models [1,12,21,23,53,71]. As these two requirements are always contradictory for any kind of system identification methodology, this framework resulted in the so called fuzzy system accuracy-interpretability tradeoff [21,23].…”

Section: The Interpretability-accuracy Tradeoffmentioning

confidence: 99%

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A Historical Review of Mamdani-Type Genetic Fuzzy Systems

Cordón¹

2012

Combining Experimentation and Theory

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Abstract. The need for trading off interpretability and accuracy is intrinsic to the use of fuzzy systems. The fuzzy modeling scientific community has proposed many different design techniques dealing with the interpretability-accuracy tradeoff. In particular, the use of genetic fuzzy systems has been widely extended thanks to their inherent flexibility and their capability to jointly consider different optimization criteria. The current contribution constitutes a brief review on some of the existing genetic fuzzy system approaches relying on Mamdani-type fuzzy rule-based systems to obtain interpretable linguistic fuzzy models with a good accuracy.

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Section: O Cordónmentioning

confidence: 98%

Section: The Interpretability-accuracy Tradeoffmentioning

confidence: 99%

A Historical Review of Mamdani-Type Genetic Fuzzy Systems

Cordón¹

2012

Combining Experimentation and Theory

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“…On the other hand, the combination of several heterogeneous sources of knowledge (mainly expert and induced knowledge) is likely to yield compact and robust systems as pointed out by Alonso et al (2008).…”

Section: Introductionmentioning

confidence: 97%

HILK++: an interpretability-guided fuzzy modeling methodology for learning readable and comprehensible fuzzy rule-based classifiers

Alonso

Magdalena

2010

Soft Comput

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This work presents a methodology for building interpretable fuzzy systems for classification problems. We consider interpretability from two points of view: (1) readability of the system description and (2) comprehensibility of the system behavior explanations. The fuzzy modeling methodology named as Highly Interpretable Linguistic Knowledge (HILK) is upgraded. Firstly, a feature selection procedure based on crisp decision trees is carried out. Secondly, several strong fuzzy partitions are automatically generated from experimental data for all the selected inputs. For each input, all partitions are compared and the best one according to data distribution is selected. Thirdly, a set of linguistic rules are defined combining the previously generated linguistic variables. Then, a linguistic simplification procedure guided by a novel interpretability index is applied to get a more compact and general set of rules with a minimum loss of accuracy. Finally, partition tuning based on two efficient search strategies increases the system accuracy while preserving the high interpretability. Results obtained in several benchmark classification problems are encouraging because they show the ability of the new methodology for generating highly interpretable fuzzy rule-based classifiers while yielding accuracy comparable to that achieved by other methods like neural networks and C4.5. The best configuration of HILK will depend on each specific problem under consideration but it is important to remark that HILK is flexible enough (thanks to the combination of several algorithms in each modeling stage) to be easily adaptable to a wide range of problems.

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“…(b) Appropriate aggregation of those criteria (see e.g. [3,18,31,99]) and using multi-objective methods (see e.g. [57,85,86,100]).…”

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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HILK: A new methodology for designing highly interpretable linguistic knowledge bases using the fuzzy logic formalism

Cited by 88 publications

References 41 publications

A Historical Review of Mamdani-Type Genetic Fuzzy Systems

A Historical Review of Mamdani-Type Genetic Fuzzy Systems

HILK++: an interpretability-guided fuzzy modeling methodology for learning readable and comprehensible fuzzy rule-based classifiers

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

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