A comparative study of missing value estimation methods: Which method performs better?

Lim, Eng Aik; Zainuddin, Zarita

doi:10.1109/iced.2008.4786656

Cited by 6 publications

(2 citation statements)

References 14 publications

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“…[31,32] . There are three common ways to deal with missing values: use the feature that contains the missing value directly, delete the feature that contains the missing value (this only works if the feature contains blank values in a big number), and complete the missing value [33,34] . Because there are a small number of blank values in the raw Dataset, the features containing blank values are employed in this paper to process blank values.…”

Section: Data Pretreatmentmentioning

confidence: 99%

Automated machine learning structure-composition-property relationships of perovskite materials for energy conversion and storage

Qin¹,

Lin²

2022

Energy Mater

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Section: Data Pretreatmentmentioning

confidence: 99%

Automated machine learning structure-composition-property relationships of perovskite materials for energy conversion and storage

Qin¹,

Lin²

2022

Energy Mater

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“…The BPCA technique used in that study (i.e., Oba et al 2003) for missing data preprocessing was found to be quite good even when 40% of data were missing, surpassing the performance of the models on the basis of k-nearest neighbors (kNN) and singular value decomposition (SVD). The tool was also used by Lim and Zainuddin (2008), which compared the BPCA with the local least square imputation (LLS) and the radial basis function (RBF) network. They concluded that BPCA had the best results according to the normalized root mean squared error (NRMSE) criterion, and the LLS produced competitive results when compared with BPCA, followed by RBF, which had the lowest performance.…”

Section: Datamentioning

confidence: 99%

Hybrid Water Demand Forecasting Model Associating Artificial Neural Network with Fourier Series

Odan

Reis

2012

J. Water Resour. Plann. Manage.

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This paper addressed the problem of water-demand forecasting for real-time operation of water supply systems. The present study was conducted to identify the best fit model using hourly consumption data from the water supply system of Araraquara, São Paulo, Brazil. Artificial neural networks (ANNs) were used in view of their enhanced capability to match or even improve on the regression model forecasts. The ANNs used were the multilayer perceptron with the back-propagation algorithm (MLP-BP), the dynamic neural network (DAN2), and two hybrid ANNs. The hybrid models used the error produced by the Fourier series forecasting as input to the MLP-BP and DAN2, called ANN-H and DAN2-H, respectively. The tested inputs for the neural network were selected literature and correlation analysis. The results from the hybrid models were promising, DAN2 performing better than the tested MLP-BP models. DAN2-H, identified as the best model, produced a mean absolute error (MAE) of 3:3 L∕s and 2:8 L∕s for training and test set, respectively, for the prediction of the next hour, which represented about 12% of the average consumption. The best forecasting model for the next 24 hours was again DAN2-H, which outperformed other compared models, and produced a MAE of 3:1 L∕s and 3:0 L∕s for training and test set respectively, which represented about 12% of average consumption.

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A Robust Learning Model for Dealing with Missing Values in Many-Core Architectures

Lopes

Ribeiro

2011

Adaptive and Natural Computing Algorithms

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A comparative study of missing value estimation methods: Which method performs better?

Cited by 6 publications

References 14 publications

Automated machine learning structure-composition-property relationships of perovskite materials for energy conversion and storage

Automated machine learning structure-composition-property relationships of perovskite materials for energy conversion and storage

Hybrid Water Demand Forecasting Model Associating Artificial Neural Network with Fourier Series

A Robust Learning Model for Dealing with Missing Values in Many-Core Architectures

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