Multi-objective genetic learning of serial hierarchical fuzzy systems for large-scale problems

Benítez, Alicia D.; Casillas, Jorge

doi:10.1007/s00500-012-0909-2

Cited by 32 publications

(19 citation statements)

References 52 publications

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“…The best individual in the population is the solution which minimizes equation (10). GPFISRegress tries to reduce the complexity of the rule base by employing a simple heuristic: Lexicographic Parsimony Pressure [18].…”

Section: Discussionmentioning

confidence: 99%

“…As a GFS integrates a Fuzzy Inference System (FIS) and a Genetic Based Meta-Heuristic (GBMH), it provides fair accuracy and linguistic interpretability (FIS component) through the automatic learning of its parameters/rules (GBMH component). Works on GFSs applied to regression problems are mostly based on improving the Genetic Based Meta-Heuristic counterpart of GFSs by using Multi-Objective Evolutionary Algorithms [3,[7][8][9][10]. In general most of these works do not explore linguistic hedges and negation operators.…”

Section: Introductionmentioning

confidence: 99%

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A Multi-gene Genetic Programming Fuzzy Inference System for Regression Problems

Koshiyama¹,

Vellasco²,

Tanscheit³

2015

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

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This work presents a novel Genetic Fuzzy System (GFS), called Genetic Programming Fuzzy Inference System for Regression problems (GPFISRegress). It makes use of Multi-Gene Genetic Programming to build the premises of fuzzy rules, including t-norms, negation and linguistic hedge operators. GPFIS-Regress also defines a consequent term that is more compatible with a given premise and makes use of aggregation operators to weigh fuzzy rules in accordance with their influence on the problem. The system has been applied to a set of benchmarks and has also been compared to other GFSs, showing competitive results in terms of accuracy and interpretability.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Multi-gene Genetic Programming Fuzzy Inference System for Regression Problems

Koshiyama¹,

Vellasco²,

Tanscheit³

2015

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

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“…By doing this, several layers are produced in HFSs. Based on the same input variables, HFSs may be produced using different topologies, e.g., serial and parallel HFS [12]. The parallel HFS can have more than one lowdimensional FLS per layer, while serial HFSs use strictly one FLS per layer as shown in Fig.…”

Section: B Hierarchical Fuzzy Systemsmentioning

confidence: 99%

“…In HFSs, the original FLSs are decomposed into a series of low-dimensional FLSs-fuzzy logic subsystems (see Section II-B). Moreover, the rules in HFSs commonly have antecedents with fewer variables than the rules in FLSs with equivalent function, since the number of input variables of each subsystem is lower [12], [13]. Thus, HFSs tend to reduce rule explosion, thus minimizing complexity, and improving model interpretability.…”

Section: Introductionmentioning

confidence: 99%

Interpretability indices for hierarchical fuzzy systems

Razak

Garibaldi

Wagner

et al. 2017

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Abstract-Hierarchical fuzzy systems (HFSs) have been shown to have the potential to improve interpretability of fuzzy logic systems (FLSs). In recent years, a variety of indices have been proposed to measure the interpretability of FLSs such as the Nauck index and Fuzzy index. However, interpretability indices associated with HFSs have not so far been discussed. The structure of HFSs, with multiple layers, subsystems, and varied topologies, is the main challenge in constructing interpretability indices for HFSs. Thus, the comparison of interpretability between FLSs and HFSs-even at the index level-is still subject to open discussion. This paper begins to address these challenges by introducing extensions to the FLS Nauck and Fuzzy interpretability indices for HFSs. Using the proposed indices, we explore the concept of interpretability in relation to the different structures in FLSs and HFSs. Initial experiments on benchmark datasets show that based on the proposed indices, HFSs with equivalent function to FLSs produce higher indices, i.e. are more interpretable than their corresponding FLSs.

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“…Benítez e Casillas [26] apresentam um SFG multi-objetivo, visandoà solução de problemas de alta dimensionalidade a partir de uma estrutura hierárquica. Este modelo se baseia na ideia de sub-sistemas fuzzy.…”

Section: Regressãounclassified

Gpfis: Um Sistema Fuzzy-Genético Genérico Baseado Em Programação Genética

KOSHIYAMA¹

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AgradecimentosAo povo brasileiro, ao CNPq e a PUC-Rio pelos auxílios e estrutura concedida, sem os quais este trabalho não poderia ter sido realizado.A minha família, em destaque meus irmãos -Wladmir e Kallyah -e pais -Adriano e Sayonara -por todos os momentos abdicados da minha presença, devido a este manuscrito e demais projetos. Agradeço e saibam que temos uma ligação de afeto para além do infinito.A minha orientadora, Professora Marley Vellasco, que sempre me estimulou a produzir cientificamente, me incentivando nas inúmeras reuniões e revisões de texto, além da oportunidade de trabalhar em projetos. Também ao meu co-orientador, Professor Ricardo Tanscheit, pelas excelentes conversas e histórias compartilhadas e por ter me permitido absorver um pouco do seu método "minimalista". A ambos o meu maior respeito e admiração.Aos Também a pessoas com quem pude mais aprender do que transmitir, e com as quais formou-se uma amizade sólida. Iury Steiner, João Zanni, Pedro Milet e Douglas Dias, a vocês obrigado por todas as risadas e experiência passada. Ainda, a Tatiana Escovedo e Rubens Nascimento que me adotaram como filho e partilharam comigo trabalhos, pizzas e viagens.Aos professores Marco Pacheco, Wagner Tassinari,Álvaro Veiga, Carlos Tomei e Maria Lorenzon, dos quais pude absorver, cada um a sua maneira, a forma de lidar e transmitir a ciência, sempre com rigor e crítica, e me permitiram também vislumbrar a aplicação desta fora dos muros da universidade. In memorian a Padre Libanio pela amizade partilhada e exemplo de vida.Por fim, ao corpo de funcionários do Departamento de Engenharia Elétrica, notavelmente Alcina, Mara, Márcia, Isnarde e Miriam pelo apoio.

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Multi-objective genetic learning of serial hierarchical fuzzy systems for large-scale problems

Cited by 32 publications

References 52 publications

A Multi-gene Genetic Programming Fuzzy Inference System for Regression Problems

A Multi-gene Genetic Programming Fuzzy Inference System for Regression Problems

Interpretability indices for hierarchical fuzzy systems

Gpfis: Um Sistema Fuzzy-Genético Genérico Baseado Em Programação Genética

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