Regularized extreme learning machine for regression with missing data

Qi, Yu; Miché, Yoan; Eirola, Emil; Heeswijk, Mark van; Séverin, Éric; Lendasse, Amaury

doi:10.1016/j.neucom.2012.02.040

Cited by 112 publications

(33 citation statements)

References 26 publications

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“…En la literatura se han escrito varios trabajos sobre cómo controlar el sobreajuste, tanto para redes neuronales (especialmente MLP) como para otros modelos de aprendizaje de máquina; entre estos se encuentran: [7][8][9][10][11][12]. En los MLP el problema de sobreajuste es controlado, principalmente, mediante tres estrategias de regularización: descomposición de pesos (weightdecay) propuesta por [8], eliminación de pesos (weight elimination) por [13] (ambas son estrategias de reducción y son descritas en [9]), y regresión en cadena (ridge regression) propuesta por [14].…”

Section: Controlando El Sobreajuste De Una Red Cascorunclassified

Control del sobreajuste en redes neuronales tipo cascada correlación aplicado a la predicción de precios de contratos de electricidad

Villa

Velásquez

Sánchez

2015

rev.ing.univ.Medellin

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ResumenLa predicción de precios de electricidad es considerada una tarea difí-cil debido a la cantidad y complejidad de los factores que influyen en su representación, y sus relaciones. Las redes neuronales tipo cascada correlación -CASCOR-permiten, realizar un aprendizaje constructivo, capturando mejor las características de los datos; sin embargo, presentan una alta tendencia al sobreajuste. Para el control del sobreajuste en algunos ámbitos se usan técnicas de regularización. No obstante, en la literatura no existen estudios que: i) Utilicen técnicas de regularización para el control de sobreajuste en redes CASCOR; ii) Usen redes CASCOR en la predicción de series de electricidad; iii) comparen el desempeño con redes neuronales tradicionales o modelos estadísticos. El objetivo de este artículo es modelar y predecir el comportamiento de la serie de precios de contratos de electricidad en Colombia, usando redes CASCOR y controlando el sobreajuste con técnicas de regularización.Palabras clave: pronóstico de series de tiempo, redes cascada correlación, rede neuronales, mercado de electricidad colombiano

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Section: Controlando El Sobreajuste De Una Red Cascorunclassified

Control del sobreajuste en redes neuronales tipo cascada correlación aplicado a la predicción de precios de contratos de electricidad

Villa

Velásquez

Sánchez

2015

rev.ing.univ.Medellin

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“…In order to deal with IPDU big data problems, some experts and scholars turned their research directions to data integration based on machine learning. Yu and et al proposed an extreme learning machine based missing data completion method [16]. Li and Socher introduced deep learning theory to fulfill the incomplete data restoration and integration tasks, respectively [17,18].…”

Section: Introductionmentioning

confidence: 99%

Generative Adversarial Networks Based Heterogeneous Data Integration and Its Application for Intelligent Power Distribution and Utilization

Tan

Liu

et al. 2018

Applied Sciences

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Featured Application: Authors are encouraged to provide a concise description of the specific application or a potential application of the work. This section is not mandatory.Abstract: Heterogeneous characteristics of a big data system for intelligent power distribution and utilization have already become more and more prominent, which brings new challenges for the traditional data analysis technologies and restricts the comprehensive management of distribution network assets. In order to solve the problem that heterogeneous data resources of power distribution systems are difficult to be effectively utilized, a novel generative adversarial networks (GANs) based heterogeneous data integration method for intelligent power distribution and utilization is proposed. In the proposed method, GANs theory is introduced to expand the distribution of completed data samples. Then, a so-called peak clustering algorithm is proposed to realize the finite open coverage of the expanded sample space, and repair those incomplete samples to eliminate the heterogeneous characteristics. Finally, in order to realize the integration of the heterogeneous data for intelligent power distribution and utilization, the well-trained discriminator model of GANs is employed to check the restored data samples. The simulation experiments verified the validity and stability of the proposed heterogeneous data integration method, which provides a novel perspective for the further data quality management of power distribution systems.

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“…Moreover, the modified ELM models can validly solve the problems of imbalance sample and obtain better performance [25][26][27][28]. Therefore, the modified ELM methods have become the main research direction.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Method of Check Valve Based on Multikernel Cost-Sensitive Extreme Learning Machine

Wang

2017

Complexity

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Check valve is one of the most important components and most easily damaged parts in high pressure diaphragm pump, which is a typical representative of reciprocating machinery. In order to ensure the normal operation of the pump, it is necessary to monitor its running state and diagnose fault. However, in the fault diagnosis of check valve, the classification models with single kernel function can not fully interpret the classification decision function, and meanwhile unreasonable assumption of diagnostic cost equalization has a significant impact on classification results. Therefore, the multikernel function and cost-sensitive mechanism are introduced to construct the fault diagnosis model of check valve based on the multikernel cost-sensitive extreme learning machine (MKL-CS-ELM) in this paper. The comparative test results of check valve for high pressure diaphragm pump show that MKL-CS-ELM can obtain fairly or slightly better performance than ELM, CS-ELM, MKL-ELM, and multikernel cost-sensitive support vector learning machine (MKL-CS-SVM). At the same time, the presented method can obtain very high accuracy under imbalance datasets condition and effectively overcome the weakness of diagnostic cost equalization and improve the interpretability and reliability of the decision function of classification model. It, therefore, is more suitable for the practical application.

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Regularized extreme learning machine for regression with missing data

Cited by 112 publications

References 26 publications

Control del sobreajuste en redes neuronales tipo cascada correlación aplicado a la predicción de precios de contratos de electricidad

Control del sobreajuste en redes neuronales tipo cascada correlación aplicado a la predicción de precios de contratos de electricidad

Generative Adversarial Networks Based Heterogeneous Data Integration and Its Application for Intelligent Power Distribution and Utilization

Fault Diagnosis Method of Check Valve Based on Multikernel Cost-Sensitive Extreme Learning Machine

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