Genetic lateral tuning for subgroup discovery with fuzzy rules using the algorithm NMEEF-SD

Carmona, C. J.; González, Pedro; Gacto, María José; Jesus, María José del

doi:10.1080/18756891.2012.685323

Cited by 7 publications

(4 citation statements)

References 42 publications

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“…This approach was presented in and allows the lateral displacement of the labels considering only one parameter (slight displacements to the left/right of the original membership functions). This involves a simplification of the search space that eases the derivation of optimal models.…”

Section: Evolutionary Algorithms For Sdmentioning

confidence: 99%

Overview on evolutionary subgroup discovery: analysis of the suitability and potential of the search performed by evolutionary algorithms

Carmona

González

Jesus

et al. 2014

WIREs Data Min & Knowl

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Subgroup discovery (SD) is a descriptive data mining technique using supervised learning. In this article, we review the use of evolutionary algorithms (EAs) for SD. In particular, we will focus on the suitability and potential of the search performed by EAs in the development of SD algorithms. Future directions in the use of EAs for SD are also presented in order to show the advantages and benefits that this search strategy contribute to this task.

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Section: Evolutionary Algorithms For Sdmentioning

confidence: 99%

Overview on evolutionary subgroup discovery: analysis of the suitability and potential of the search performed by evolutionary algorithms

Carmona

González

Jesus

et al. 2014

WIREs Data Min & Knowl

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“…The genetic tuning of the lateral position of the linguistic labels 22 has proved to provide very accurate models 24,55,56 . This tuning approach, which is based on the linguistic 2-tuples representation 57 , allows the lateral displacement of the labels considering only one parameter (slight displacements to the left/right of the original membership functions).…”

Section: Using the Tuning Of The Lateral Position Of The Membership Fmentioning

confidence: 99%

Iivfdt: Ignorance Functions Based Interval-Valued Fuzzy Decision Tree With Genetic Tuning

Sanz

Bustince

Fernández

et al. 2012

Int. J. Unc. Fuzz. Knowl. Based Syst.

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The choice of membership functions plays an essential role in the success of fuzzy systems. This is a complex problem due to the possible lack of knowledge when assigning punctual values as membership degrees. To face this handicap, we propose a methodology called Ignorance functions based Interval-Valued Fuzzy Decision Tree with genetic tuning, IIVFDT for short, which allows to improve the performance of fuzzy decision trees by taking into account the ignorance degree. This ignorance degree is the result of a weak ignorance function applied to the punctual value set as membership degree. Our IIVFDT proposal is composed of four steps: (1) the base fuzzy decision tree is generated using the fuzzy ID3 algorithm; (2) the linguistic labels are modeled with Interval-Valued Fuzzy Sets. To do so, a new parametrized construction method of Interval-Valued Fuzzy Sets is defined, whose length represents such ignorance degree; (3) the fuzzy reasoning method is extended to work with this representation of the linguistic terms; (4) an evolutionary tuning step is applied for computing the optimal ignorance degree for each Interval-Valued Fuzzy Set. The experimental study shows that the IIVFDT method allows the results provided by the initial fuzzy ID3 with and without Interval-Valued Fuzzy Sets to be outperformed. The suitability of the proposed methodology is shown with respect to both several state-of-the-art fuzzy decision trees and C4.5. Furthermore, we analyze the quality of our approach versus two methods that learn the fuzzy decision tree using genetic algorithms. Finally, we show that a superior performance can be achieved by means of the positive synergy obtained when applying the well known genetic tuning of the lateral position after the application of the IIVFDT method.

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“…The approach is based on the NSGA-II. In [116], a post processing approach for improving the results of algorithm NMEEF-SD in a sub group discovery is proposed. It allows the partitions to be adapted in the context of variables.…”

Section: Emo Approaches For Data Mining Applicationsmentioning

confidence: 99%

A Review on the Interpretability-Accuracy Trade-Off in Evolutionary Multi-Objective Fuzzy Systems (EMOFS)

Shukla

Tripathi

2012

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Interpretability and accuracy are two important features of fuzzy systems which are conflicting in their nature. One can be improved at the cost of the other and this situation is identified as “Interpretability-Accuracy Trade-Off”. To deal with this trade-off Multi-Objective Evolutionary Algorithms (MOEA) are frequently applied in the design of fuzzy systems. Several novel MOEA have been proposed and invented for this purpose, more specifically, Non-Dominated Sorting Genetic Algorithms (NSGA-II), Strength Pareto Evolutionary Algorithm 2 (SPEA2), Fuzzy Genetics-Based Machine Learning (FGBML), (2 + 2) Pareto Archived Evolutionary Strategy ((2 + 2) PAES), (2 + 2) Memetic- Pareto Archived Evolutionary Strategy ((2 + 2) M-PAES), <em>etc</em>. This paper introduces and reviews the approaches to the issue of developing fuzzy systems using Evolutionary Multi-Objective Optimization (EMO) algorithms considering ‘Interpretability-Accuracy Trade-off’ and mainly focusing on the work in the last decade. Different research issues and challenges are also discussed

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Genetic lateral tuning for subgroup discovery with fuzzy rules using the algorithm NMEEF-SD

Cited by 7 publications

References 42 publications

Overview on evolutionary subgroup discovery: analysis of the suitability and potential of the search performed by evolutionary algorithms

Overview on evolutionary subgroup discovery: analysis of the suitability and potential of the search performed by evolutionary algorithms

Iivfdt: Ignorance Functions Based Interval-Valued Fuzzy Decision Tree With Genetic Tuning

A Review on the Interpretability-Accuracy Trade-Off in Evolutionary Multi-Objective Fuzzy Systems (EMOFS)

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