Estratégias para modelagem de dados multivariados na presença de correlação

Fogliatto, Flávio Sanson

doi:10.1590/s0104-530x2000000100002

Cited by 4 publications

(5 citation statements)

References 9 publications

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“…By adjusting models to identify complex patterns and relationships between variables, these algorithms can predict and explain substantial variations in the data, including anomalies and outliers. Training on diverse data sets and optimizing hyperparameters allow these techniques to create dynamic models that can identify anomalous behavior or outliers, thereby enhancing the robustness and accuracy of data analysis (Fogliatto 2000).…”

Section: Exploratory Analysismentioning

confidence: 99%

“…These correlations provide valuable insights for developing predictive models of concrete strength, particularly when using ML techniques (Fogliatto 2000). When creating a regression model, we consider the variables with the strongest correlations as important features for predicting the compressive strength of concrete (Young et al 2019).…”

Section: Correlation Analysismentioning

confidence: 99%

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Comparing the performance of machine learning models for predicting the compressive strength of concrete

Loureiro,

Stefani

2024

Preprint

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This study aimed to investigate and compare the performance of different machine learning models in predicting the compressive strength of concrete using a data set of 1234 compressive strength values. The predictive variables were selected based on their relevance using the SelectKBest method, resulting in an analysis of eight and six predictive variables. The evaluation was conducted through linear correlation studies via simple linear regression and non-linear correlation studies using support vector regression (SVR), gradient boosting (GB), and artificial neural networks (ANN). The results showed a coefficient of determination (R²) = 0.85 and a root mean square error (RMSE) = 30.9051 MPa for SVR, R² = 0.90 and RMSE = 25.5979 MPa for GB, and R² = 0.87 and RMSE = 5.781 MPa for ANN. The comparison between the machine learning methods revealed significant differences. For instance, GB stood out with a higher R² value, demonstrating its remarkable ability to explain the variability in the data. Conversely, ANN showed the lowest RMSE value, indicating notable accuracy in the predictions. The choice between these approaches depends on considerations regarding the balance between explainability and accuracy. While GB provides a more in-depth understanding of the relationship between variables, ANN stands out for the accuracy of its predictions.

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Section: Exploratory Analysismentioning

confidence: 99%

Section: Correlation Analysismentioning

confidence: 99%

Comparing the performance of machine learning models for predicting the compressive strength of concrete

Loureiro,

Stefani

2024

Preprint

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“…A ARL consiste, em sua essência, na determinação de uma equação ou modelo que descreva de maneira eficiente o efeito de um grupo de variáveis independentes sobre uma ou mais variáveis dependentes. Conforme Fogliatto (2000), a aplicação da técnica de modelagem por regressão linear a um grupo de dados resulta na determinação de coeficientes lineares que ponderam o efeito de variáveis independentes (tempo, carga de uso) sobre variáveis dependentes (desgaste, falha). Modelos com uma única variável dependente são ditos univariados.…”

Section: Modelagem Computacional Para Sistemas De Manutençãounclassified

Modelo de simulação para captura e aproveitamento de ociosidade para manutenção em sistemas de expedição de minérios

Faría

Ribeiro

Viana

2016

Revista PO: R. Eletr. de Eng. de Produção e Correlatas

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Este trabalho apresenta o desenvolvimento de um simulador que permite prever janelas de oportunidade para manutenção em sistemas de expedição de produtos de mineração. O simulador foi desenvolvido considerando o caso da mina de Carajás no Pará, uma das maiores do país. Para tal, foi elaborado um modelo heurístico para prever as janelas de oportunidade, de forma a capturá-las para atividades de manutenção. Esse modelo foi baseado em conhecimentos prévios do pesquisador sobre o problema e estudo da lógica de operação do sistema, dando origem a um aplicativo que foi nomeado Mosaico de Simulação da Expedição. Testes realizados com dados históricos do sistema de expedição mostraram que a diferença entre o tempo real de ociosidades ocorridas no período histórico e o tempo simulado de ociosidades para o mesmo período foi de aproximadamente 10% a favor da segurança (tempo previsto pelo Mosaico menor que o tempo real disponível para atividades de manutenção). Esta diferença foi considerada aceitável, e confirmou a utilidade do Mosaico como suporte ao planejamento das atividades de manutenção, buscando o aproveitamento das oportunidades e, consequentemente, melhorar o desempenho do sistema em estudo.

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“…The appropriate model must allow interpreting the situation, getting estimates and making predictions. Application of the modeling technique by linear regression to a group of data results in the determination of linear coefficients, weighting the effect of independent variables on the dependent variable (FOGLIATTO, 2000). The linear regression model is shown in equation (15), representing the relationship between the dependent variable y and the k independent variables X (MONTGOMERY et al, 2001).…”

Section: Regression Control Chartsmentioning

confidence: 99%

Capability Indices for Control Charts Based on Regression Models

Souza

Pedrini

Caten

2015

Braz. J. of Operations & Production Manag.

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Process capability analysis is extremely important for optimization and quality improvement. It verifies whether the process under analysis is capable of producing items within engineering and customers' specifications. The use of capability indices when assumptions are not satisfied leads to erroneous conclusions, compromising the study and analysis of the process, jeopardizing the fulfillment of requirements from management or external customers. Aiming at filling a gap identified in the literature, the main contributions of this work are: (i) proposition of capability indices for processes monitored through control charts based on regression models, for symmetric and asymmetric specifications; and (ii) comparison of the proposed indices with traditional capability indices through a simulated process. Palavras-chave: Process control. Regression models. Capability indices.Abstract would have to be settled for each new setting, in addition to the difficulty of calculating control limits due to the low number of samples of each batch manufactured in each setting. In these cases, the response variable (dependent variable) of a product or process is best represented by a mathematical equation that models its relationship with the control variables of the process (JACOBI et al., 2002;SHU et al., 2004). Mandel (1969) proposed the regression control chart, a combination of control chart techniques and simple linear regression models. This chart is used in processes in which the effect of a response variable is a function of a control variable. Initially a regression model is generated, which represents the relationship between the response variable and the control variable, and subsequently the residual is monitored through the model. Traditional control charts are not able to perform analyses when the response variable is dependent on the control variable, as a constant average over time is presumed.Mandel's original proposal (1969) can only be applied in processes involving a control variable. Haworth (1996) extended Mandel's proposal (1969) by suggesting the multiple regression chart, consisting in the estimation of a multiple linear regression model and monitoring of standardized residuals. However, Haworth's work (1996)

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Estratégias para modelagem de dados multivariados na presença de correlação

Cited by 4 publications

References 9 publications

Comparing the performance of machine learning models for predicting the compressive strength of concrete

Comparing the performance of machine learning models for predicting the compressive strength of concrete

Modelo de simulação para captura e aproveitamento de ociosidade para manutenção em sistemas de expedição de minérios

Capability Indices for Control Charts Based on Regression Models

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