Extreme learning machine for missing data using multiple imputations

Sovilj, Dušan; Eirola, Emil; Miché, Yoan; Björk, Kaj‐Mikael; Nian, Rui; Akusok, Anton; Lendasse, Amaury

doi:10.1016/j.neucom.2015.03.108

Cited by 102 publications

(54 citation statements)

References 43 publications

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“…In [23] a methodology based on Gaussian Mixture Model (GMM) and Extreme Learning Machine (ELM) is developed and tested on some datasets from the UCI Machine Learning Repository and the LIACC regression repository. GMM is used to model the data distribution which is adapted to handle missing values, while ELM enables devising a Multiple Imputation strategy for final estimation.…”

Section: Missing Values and Relatedmentioning

confidence: 99%

Neural Models for Imputation of Missing Ozone Data in Air‐Quality Datasets

et al. 2018

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Ozone is one of the pollutants with most negative effects on human health and in general on the biosphere. Many data-acquisition networks collect data about ozone values in both urban and background areas. Usually, these data are incomplete or corrupt and the imputation of the missing values is a priority in order to obtain complete datasets, solving the uncertainty and vagueness of existing problems to manage complexity. In the present paper, multiple-regression techniques and Artificial Neural Network models are applied to approximate the absent ozone values from five explanatory variables containing air-quality information. To compare the different imputation methods, real-life data from six data-acquisition stations from the region of Castilla y León (Spain) are gathered in different ways and then analyzed. The results obtained in the estimation of the missing values by applying these techniques and models are compared, analyzing the possible causes of the given response.

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Section: Missing Values and Relatedmentioning

confidence: 99%

Neural Models for Imputation of Missing Ozone Data in Air‐Quality Datasets

et al. 2018

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“…The importance of data recovery in numerous fields such as medical [15,16], neuro-computation [17], and climate science [18,19] has long been realized. Several approaches have been applied to recover the value of missing data.…”

Section: Past Research On Data Recoverymentioning

confidence: 99%

“…Extreme learning machine [17] Less training time compared to BP and SVM/SVR; outperforms BP in many applications. Can over fit and get trapped in local minima.…”

Section: Savitzy-golay (S-g) [5]mentioning

confidence: 99%

Optical Cloud Pixel Recovery via Machine Learning

et al. 2017

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Remote sensing derived Normalized Difference Vegetation Index (NDVI) is a widely used index to monitor vegetation and land use change. NDVI can be retrieved from publicly available data repositories of optical sensors such as Landsat, Moderate Resolution Imaging Spectro-radiometer (MODIS) and several commercial satellites. Studies that are heavily dependent on optical sensors are subject to data loss due to cloud coverage. Specifically, cloud contamination is a hindrance to long-term environmental assessment when using information from satellite imagery retrieved from visible and infrared spectral ranges. Landsat has an ongoing high-resolution NDVI record starting from 1984. Unfortunately, this long time series NDVI data suffers from the cloud contamination issue. Though both simple and complex computational methods for data interpolation have been applied to recover cloudy data, all the techniques have limitations. In this paper, a novel Optical Cloud Pixel Recovery (OCPR) method is proposed to repair cloudy pixels from the time-space-spectrum continuum using a Random Forest (RF) trained and tested with multi-parameter hydrologic data. The RF-based OCPR model is compared with a linear regression model to demonstrate the capability of OCPR. A case study in Apalachicola Bay is presented to evaluate the performance of OCPR to repair cloudy NDVI reflectance. The RF-based OCPR method achieves a root mean squared error of 0.016 between predicted and observed NDVI reflectance values. The linear regression model achieves a root mean squared error of 0.126. Our findings suggest that the RF-based OCPR method is effective to repair cloudy pixels and provides continuous and quantitatively reliable imagery for long-term environmental analysis.

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“…At present, big data mining technology is widely used in various fields, such as geographic analysis [10,16], financial analysis, smart city and biotechnology. It usually needs a better dataset to support the research, but in fact there is always noise data or missing value data in the datasets [9,14,17]. In order to improve data quality, various machine learning algorithms [7,15] are often required to estimate the missing value and clean noise data.…”

Section: Introductionmentioning

confidence: 99%

Adaptive RBF Interpolation for Estimating Missing Values in Geographical Data

Gao

Mei

Cuomo

et al. 2020

Lecture Notes in Computer Science

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The quality of datasets is a critical issue in big data mining. More interesting things could be mined from datasets with higher quality. The existence of missing values in geographical data would worsen the quality of big datasets. To improve the data quality, the missing values are generally needed to be estimated using various machine learning algorithms or mathematical methods such as approximations and interpolations. In this paper, we propose an adaptive Radial Basis Function (RBF) interpolation algorithm for estimating missing values in geographical data. In the proposed method, the samples with known values are considered as the data points, while the samples with missing values are considered as the interpolated points. For each interpolated point, first, a local set of data points are adaptively determined. Then, the missing value of the interpolated point is imputed via interpolating using the RBF interpolation based on the local set of data points. Moreover, the shape factors of the RBF are also adaptively determined by considering the distribution of the local set of data points. To evaluate the performance of the proposed method, we compare our method with the commonly used k Nearest Neighbors (kNN) interpolation and Adaptive Inverse Distance Weighted (AIDW) methods, and conduct three groups of benchmark experiments. Experimental results indicate that the proposed method outperforms the kNN interpolation and AIDW in terms of accuracy, but worse than the kNN interpolation and AIDW in terms of efficiency.

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Extreme learning machine for missing data using multiple imputations

Cited by 102 publications

References 43 publications

Neural Models for Imputation of Missing Ozone Data in Air‐Quality Datasets

Neural Models for Imputation of Missing Ozone Data in Air‐Quality Datasets

Optical Cloud Pixel Recovery via Machine Learning

Adaptive RBF Interpolation for Estimating Missing Values in Geographical Data

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