A multi-objective genetic optimization of interpretability-oriented fuzzy rule-based classifiers

Rudziński, Filip

doi:10.1016/j.asoc.2015.09.038

Cited by 68 publications

(23 citation statements)

References 81 publications

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“…More precisely, FRBSs are again obtained by means of GAs, with CHC applied to tune intervalvalued fuzzy systems (Antonio Sanz et al, 2013Sanz et al, , 2014 which is lately applied to imbalanced classification (Antonio Sanz et al, 2015), or with a GA applied in a multi-stage method for rule optimization (Nguyen et al, 2015). Interpretability of FRBSs is also taken into account (Rudzinski, 2016) by means of MOEAs this past year. Interval-valued fuzzy kNN have been also obtained by means of CHC optimization (Derrac et al, 2016), as well as sparse fuzzy inference systems were obtained by the application of a memetic GA (Serdio et al, 2017).…”

Section: Classification (Second Period)mentioning

confidence: 99%

Evolutionary data mining and applications: A revision on the most cited papers from the last 10 years (2007–2017)

Alcalá

Gacto

Alcalá‐Fdez

2017

WIREs Data Min & Knowl

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The ability of evolutionary algorithms (EAs) to manage a set of solutions, even attending multiple objectives, as well as their ability to optimize any kinds of values, allows them to fit very well some parts of the data‐mining (DM) problems, whose native learning techniques usually associated with the inherent DM problem are not able to solve. Therefore, EAs are widely applied to complement or even replace the classical DM learning approaches. This application of EAs to the DM process is usually named evolutionary data mining (EDM). This contribution aims at showing a glimpse of the EDM field current state by focusing on the most cited papers published in the last 10 years. A descriptive analysis of the papers together with a bibliographic study is performed in order to differentiate past and current trends and to easily focus on significant further developments. Results show that, in the case of the most cited studied papers, the use of EAs on DM tasks is mainly focused on enhancing the classical learning techniques, thus completely replacing them only when it is directly motivated by the nature of problem. The bibliographic analysis is also showing that even though EAs were the main techniques used for EDM, the emergent evolutionary computation algorithms (swarm intelligence, etc.) are becoming nowadays the most cited and used ones. Based on all these facts, some potential further directions are also discussed. WIREs Data Mining Knowl Discov 2018, 8:e1239. doi: 10.1002/widm.1239 This article is categorized under: Fundamental Concepts of Data and Knowledge > Knowledge Representation Technologies > Computational Intelligence Technologies > Classification Technologies > Prediction

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Section: Classification (Second Period)mentioning

confidence: 99%

Evolutionary data mining and applications: A revision on the most cited papers from the last 10 years (2007–2017)

Alcalá

Gacto

Alcalá‐Fdez

2017

WIREs Data Min & Knowl

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“…In the FRBC design based on the fuzzy set theory approaches [1,2,6,7,21,22,23,24,35,36,38,39,41], the fuzzy partitions from which fuzzy rules are extracted are commonly pre-designed using fuzzy sets and then linguistic terms are intuitively assigned to fuzzy sets. Furthermore, fuzzy partitions can be generated automatically from data by using discretization or granular computing mechanisms [37].…”

Section: Introductionmentioning

confidence: 99%

A Hedge Algebras Based Classification Reasoning Method With Multi-Granularity Fuzzy Partitioning

Phong

Dư

Thuy

et al. 2019

JCC

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During last years, lots of the fuzzy rule based classifier (FRBC) design methods have been proposed to improve the classification accuracy and the interpretability of the proposed classification models. Most of them are based on the fuzzy set theory approach in such a way that the fuzzy classification rules are generated from the grid partitions combined with the pre-designed fuzzy partitions using fuzzy sets. Some mechanisms are studied to automatically generate fuzzy partitions from data such as discretization, granular computing, etc. Even those, linguistic terms are intuitively assigned to fuzzy sets because there is no formalisms to link inherent semantics of linguistic terms to fuzzy sets. In view of that trend, genetic design methods of linguistic terms along with their (triangular and trapezoidal) fuzzy sets based semantics for FRBCs, using hedge algebras as the mathematical formalism, have been proposed. Those hedge algebras-based design methods utilize semantically quantifying mapping values of linguistic terms to generate their fuzzy sets based semantics so as to make use of fuzzy sets based-classification reasoning methods proposed in design methods based on fuzzy set theoretic approach for data classification. If there exists a classification reasoning method which bases merely on semantic parameters of hedge algebras, fuzzy sets-based semantics of the linguistic terms in fuzzy classification rule bases can be replaced by semantics - based hedge algebras. This paper presents a FRBC design method based on hedge algebras approach by introducing a hedge algebra- based classification reasoning method with multi-granularity fuzzy partitioning for data classification so that the semantic of linguistic terms in rule bases can be hedge algebras-based semantics. Experimental results over 17 real world datasets are compared to existing methods based on hedge algebras and the state-of-the-art fuzzy sets theoretic-based approaches, showing that the proposed FRBC in this paper is an effective classifier and produces good results.

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“…, where rand(·) returns a random number from the assumed interval and [x i,min , x i,max ] is a range of the domain of the selected set [17]. New values of σ and ρ are calculated from (13).…”

Section: Mutation Operator For Db Transformation (M-db)mentioning

confidence: 99%

“…New values of d-and e-parameters are calculated as linear combinations of their old values from both sets; they also must fulfil condition (14) [17]. New values of σ-and ρ-parameters are calculated from (13) using new values of parameters d and e.…”

Section: Crossover Operator For Db Transformation (C-db)mentioning

confidence: 99%

See 1 more Smart Citation

Handling fuzzy systems’ accuracy-interpretability trade-off by means of multi-objective evolutionary optimization methods – selected problems

Gorzałczany¹,

Rudziński²

2015

Bulletin of the Polish Academy of Sciences Technical Sciences

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Abstract. The paper addresses several open problems regarding the automatic design of fuzzy rule-based systems (FRBSs) from data using multi-objective evolutionary optimization algorithms (MOEOAs). In particular, we propose: a) new complexity-related interpretability measure, b) efficient strong-fuzzy-partition implementation for improving semantics-related interpretability, c) special-coding-free implementation of rule base and original genetic operators for its processing, and d) implementation of our ideas in the context of well-known MOEOAs such as SPEA2 and NSGA-II. The experiments demonstrate that our approach is an effective tool for handling FRBSs' accuracy-interpretability trade-off, i.e, designing FRBSs characterized by various levels of such a trade-off (in particular, for designing highly interpretability-oriented systems of still competitive accuracy).

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A multi-objective genetic optimization of interpretability-oriented fuzzy rule-based classifiers

Cited by 68 publications

References 81 publications

Evolutionary data mining and applications: A revision on the most cited papers from the last 10 years (2007–2017)

Evolutionary data mining and applications: A revision on the most cited papers from the last 10 years (2007–2017)

A Hedge Algebras Based Classification Reasoning Method With Multi-Granularity Fuzzy Partitioning

Handling fuzzy systems’ accuracy-interpretability trade-off by means of multi-objective evolutionary optimization methods – selected problems

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