Multiple Disjunctions Rule Genetic Algorithm (MDRGA): Inferring Non-Linear IF-THEN Rules in Non-Linear Datasets

Matos, Maicon Douglas Santos; Amaral, Laurence Rodrigues do

doi:10.1109/cec.2018.8477690

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…Computational intelligence includes neural network, evolutionary computation, and fuzzy computation, among which evolutionary computation includes Darwin theory, simulated society, and others [16]. Darwin's theory includes four parts: genetic algorithm, evolutionary strategy, evolutionary rules, and genetic rules [17]. Simulated society includes ant colony algorithm and particle swarm optimization algorithm.…”

Section: Computational Intelligence and Design Of College Students' I...mentioning

confidence: 99%

Optimization and Design of College Students’ Innovation and Entrepreneurship System Based on Computational Intelligence

Bai¹,

Wang²,

Zhang³

2022

Wireless Communications and Mobile Computing

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At present, college students’ innovation and entrepreneurship has gradually become a hot topic nowadays, and the popularity of related knowledge of college students’ innovation and entrepreneurship is getting higher and higher, and more college students have joined in independent innovation and entrepreneurship. In order to alleviate a series of problems in college students’ innovation and entrepreneurship, we design an SD model under computational intelligence to put forward corresponding solutions to the problems faced in innovation and entrepreneurship, through the establishment of relevant models and experiments to verify that SD model is based on computational intelligence to optimize the design of college students’ innovation and entrepreneurship system. We know that the dimensions of tools include three dimensions: supply, demand, and environment. With the change of time, college students’ choice of tools for innovation and entrepreneurship has developed from a single supply tool to a multidimensional tool choice. Through the establishment of SD computational intelligence model to optimize the innovation and entrepreneurship system of college students, through the survey data, it can be seen that the prediction error value of SD model is about 9% and less than 10%, which is within the negligible error range, indicating that SD model has an optimization effect on the innovation and entrepreneurship system of college students. According to the survey, the three dimensions of capital investment, experience, and education level have an impact on college students’ innovation and entrepreneurship, accounting for 45%, 25%, and 30%, respectively. By comparing GW model, FIT model, and PBL model with SD model proposed in this paper, the data of precision, recall rate, accuracy rate, and F1 are compared and analyzed under the condition of changing the three dimensions of capital investment, experience, and education level, so as to select the best optimization model of innovation and entrepreneurship system for computational intelligence college students. Experiments show that when the capital investment increases by 10%, the relevant data of SD model is the best, with accuracy of 0.8761, recall rate of 0.9563, accuracy rate of 0.8972, and F1 of 0.8773; When the experience is increased by 10%, the optimization efficiency of SD model is the best, with its accuracy of 0.8660, recall rate of 0.9462, accuracy rate of 0.8871, and F1 of 0.8672. When the education level is increased by 10%, the optimization efficiency of SD model is the best, with accuracy of 0.8921, recall rate of 0.9762, accuracy rate of 0.8762, and F1 of 0.8861.

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Section: Computational Intelligence and Design Of College Students' I...mentioning

confidence: 99%

Optimization and Design of College Students’ Innovation and Entrepreneurship System Based on Computational Intelligence

Bai¹,

Wang²,

Zhang³

2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…Ao longo dos anos, diversos trabalhos vem sendo desenvolvidos utilizando AGs para tarefa de classificação, como pode ser visto em (FIDELIS;LOPES;FREITAS, 2000;CAR-VALHO;FREITAS, 2002;FREITAS, 2003;AMARAL et al, 2008;HRUS-CHKA, 2010;Junior, 2012;Silva; de Souza Ribeiro; do Amaral, 2013;FREITAS, 2013;JR, 2014;FREITAS, 2009;MATOS;AMARAL, 2018). Esses AGs são responsáveis por minerar regras de classificação do tipo SE-ENTÃO, com o objetivo de produzir boas taxas de classificação e regras de fácil interpretação.…”

Section: Introductionunclassified

Análise de novas abordagens para mineração de regras de classificação utilizando algoritmos genéticos

Alves¹

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Agradeço primeiramente a todos os meus familiares por todo apoio imprescindível. À minha namorada Lorrane, por estar sempre ao meu lado mesmo nos momentos mais difíceis. À meu orientador, professor Laurence, pelo exemplo de competência e profissionalismo. Aos professores do PPGCO-FACOM, pelo ensino de qualidade e o compormisso de formar profissionais de alto nível. Aos secretários Erisvaldo e Sônia, pelo excelente serviço prestado. Aos colegas, professores e técnicos do Laboratório de Bioinformática e Análises Moleculares (LBAM) campus Patos de Minas. Agradeço também a CAPES, pelo importante apoio financeiro. Todos vocês contribuiram grandiosamente para que este trabalho fosse concluído. Muito obrigado! "Só o que está morto não muda." (Edson Marques) Palavras-chave: Algoritmos Genéticos. Classificação de dados. Regras SE-ENTÃO. Cromossomo Binário. Ganho de Informação.

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Fuzzy IF–THEN Rules Refinement Method Using Neural Networks

Yeom¹,

Choi²

2019

jkiit

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Multiple Disjunctions Rule Genetic Algorithm (MDRGA): Inferring Non-Linear IF-THEN Rules in Non-Linear Datasets

Cited by 3 publications

References 18 publications

Optimization and Design of College Students’ Innovation and Entrepreneurship System Based on Computational Intelligence

Optimization and Design of College Students’ Innovation and Entrepreneurship System Based on Computational Intelligence

Análise de novas abordagens para mineração de regras de classificação utilizando algoritmos genéticos

Fuzzy IF–THEN Rules Refinement Method Using Neural Networks

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